Preprocess
Find preprocess workflow here.
Packages
library(tidyverse)
library(psych)
library(lavaan)
library(semPlot)
library(knitr)
library(corrplot)
Import data
taia <- read_csv("https://github.com/angelgardt/taia/raw/master/data/taia.csv")
str(taia)
tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ id : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
$ e_dighelp : num [1:495] 5 NA 4 3.33 3 ...
$ n_dighelp : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
$ e_socnet : num [1:495] 0 NA 3.2 3.6 4.5 ...
$ f_socnet : num [1:495] 3 NA 2.4 1.6 2 ...
$ n_socnet : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
$ gt_score : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
$ pr01 : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
$ pr02 : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
$ pr03 : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
$ pr04 : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
$ pr05 : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
$ pr06 : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
$ pr07 : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
$ pr08 : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
$ pr09 : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
$ pr10 : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
$ co01 : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
$ co02 : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
$ co03 : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
$ co04 : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
$ co05 : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
$ co06 : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
$ co07 : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
$ co08 : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
$ co09 : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
$ co10 : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
$ ut01 : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
$ ut02 : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
$ ut03 : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
$ ut04 : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
$ ut05 : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
$ ut06 : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
$ ut07 : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
$ ut08 : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
$ ut09 : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
$ ut10 : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
$ ut11 : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
$ ut12 : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
$ fa01 : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
$ fa02 : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
$ fa03 : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
$ fa04 : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
$ fa05 : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
$ fa06 : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
$ fa07 : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
$ fa08 : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
$ fa09 : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
$ fa10 : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
$ de01 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
$ de02 : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
$ de03 : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
$ de04 : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
$ de05 : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
$ de06 : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
$ de07 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
$ de08 : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
$ de09 : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
$ de10 : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
$ de11 : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
$ un01 : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
$ un02 : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
$ un03 : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
$ un04 : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
$ un05 : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
$ un06 : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
$ un07 : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
$ un08 : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
$ un09 : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
$ un10 : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
$ un11 : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
$ un12 : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
$ gt01 : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
$ gt02 : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
$ gt03 : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
$ gt04 : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
$ gt05 : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
$ gt06 : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
$ socnet : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
$ vk : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
$ fb : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
$ tw : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
$ in : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
$ tt : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
$ yt : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
$ freqvk : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
$ freqfb : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
$ freqtw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
$ freqin : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
$ freqtt : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
$ freqyt : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
$ expvk : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
$ expfb : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
$ exptw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
$ expin : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
$ exptt : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
$ expyt : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
$ dighelp : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
$ siri : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
[list output truncated]
- attr(*, "spec")=
.. cols(
.. id = col_character(),
.. e_dighelp = col_double(),
.. n_dighelp = col_double(),
.. e_socnet = col_double(),
.. f_socnet = col_double(),
.. n_socnet = col_double(),
.. gt_score = col_double(),
.. pr01 = col_double(),
.. pr02 = col_double(),
.. pr03 = col_double(),
.. pr04 = col_double(),
.. pr05 = col_double(),
.. pr06 = col_double(),
.. pr07 = col_double(),
.. pr08 = col_double(),
.. pr09 = col_double(),
.. pr10 = col_double(),
.. co01 = col_double(),
.. co02 = col_double(),
.. co03 = col_double(),
.. co04 = col_double(),
.. co05 = col_double(),
.. co06 = col_double(),
.. co07 = col_double(),
.. co08 = col_double(),
.. co09 = col_double(),
.. co10 = col_double(),
.. ut01 = col_double(),
.. ut02 = col_double(),
.. ut03 = col_double(),
.. ut04 = col_double(),
.. ut05 = col_double(),
.. ut06 = col_double(),
.. ut07 = col_double(),
.. ut08 = col_double(),
.. ut09 = col_double(),
.. ut10 = col_double(),
.. ut11 = col_double(),
.. ut12 = col_double(),
.. fa01 = col_double(),
.. fa02 = col_double(),
.. fa03 = col_double(),
.. fa04 = col_double(),
.. fa05 = col_double(),
.. fa06 = col_double(),
.. fa07 = col_double(),
.. fa08 = col_double(),
.. fa09 = col_double(),
.. fa10 = col_double(),
.. de01 = col_double(),
.. de02 = col_double(),
.. de03 = col_double(),
.. de04 = col_double(),
.. de05 = col_double(),
.. de06 = col_double(),
.. de07 = col_double(),
.. de08 = col_double(),
.. de09 = col_double(),
.. de10 = col_double(),
.. de11 = col_double(),
.. un01 = col_double(),
.. un02 = col_double(),
.. un03 = col_double(),
.. un04 = col_double(),
.. un05 = col_double(),
.. un06 = col_double(),
.. un07 = col_double(),
.. un08 = col_double(),
.. un09 = col_double(),
.. un10 = col_double(),
.. un11 = col_double(),
.. un12 = col_double(),
.. gt01 = col_double(),
.. gt02 = col_double(),
.. gt03 = col_double(),
.. gt04 = col_double(),
.. gt05 = col_double(),
.. gt06 = col_double(),
.. socnet = col_double(),
.. vk = col_double(),
.. fb = col_double(),
.. tw = col_double(),
.. `in` = col_double(),
.. tt = col_double(),
.. yt = col_double(),
.. freqvk = col_double(),
.. freqfb = col_double(),
.. freqtw = col_double(),
.. freqin = col_double(),
.. freqtt = col_double(),
.. freqyt = col_double(),
.. expvk = col_double(),
.. expfb = col_double(),
.. exptw = col_double(),
.. expin = col_double(),
.. exptt = col_double(),
.. expyt = col_double(),
.. dighelp = col_double(),
.. siri = col_double(),
.. alice = col_double(),
.. salut = col_double(),
.. oleg = col_double(),
.. alex = col_double(),
.. mia = col_double(),
.. mts = col_double(),
.. ggle = col_double(),
.. oth = col_double(),
.. oth_text = col_character(),
.. expsiri = col_double(),
.. expalice = col_double(),
.. expsalut = col_double(),
.. expoleg = col_double(),
.. expalex = col_double(),
.. expmia = col_double(),
.. expmts = col_double(),
.. expggle = col_double(),
.. expoth = col_double(),
.. expoth_text = col_character(),
.. selfdrcar = col_double(),
.. selfdrexp = col_double(),
.. selfdrsafe = col_double(),
.. eduai = col_double(),
.. eduaiexp = col_double(),
.. age = col_double(),
.. sex = col_character(),
.. edulvl1 = col_character(),
.. spec1 = col_character(),
.. edu2 = col_double(),
.. edulvl2 = col_character(),
.. spec2 = col_character(),
.. jobfield = col_character(),
.. jobpos = col_character(),
.. city = col_character()
.. )
Preparation
Vectors of TAIA items:
pr_items <- colnames(taia)[8:17]
co_items <- colnames(taia)[18:27]
ut_items <- colnames(taia)[28:39]
fa_items <- colnames(taia)[40:49]
de_items <- colnames(taia)[50:60]
un_items <- colnames(taia)[61:72]
taia_items <- colnames(taia)[8:72]
Vector of GT items:
gt_items <- colnames(taia)[73:78]
Column names for further formatting:
col_names <- c("", "Num. of obs.", "Mean", "SD",
"Median", "Trimmed Mean", "MAD",
"Min", "Max", "Range",
"Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
"Average interitem correlation", "S/N",
"Alpha SE", "Scale Mean", "Total Score SD",
"Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
"Std Cor",
"Cor Overlap Corrected",
"Cor if drop",
"Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
"Guttman's Lambda 6", "Average interitem correlation",
"S/N", "Alpha SE", "Var(r)","Median interitem correlation")
Exploratory analysis
TAIA descriptive statistics
taia %>%
select(all_of(pr_items)) %>%
describe() %>%
kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)
Table 1: Predictability
| pr01 |
1 |
495 |
2.84 |
0.99 |
3 |
2.87 |
1.48 |
0 |
5 |
5 |
-0.28 |
0.37 |
0.04 |
| pr02 |
2 |
495 |
2.73 |
0.97 |
3 |
2.77 |
1.48 |
0 |
5 |
5 |
-0.19 |
0.10 |
0.04 |
| pr03 |
3 |
495 |
2.89 |
1.01 |
3 |
2.91 |
1.48 |
0 |
5 |
5 |
-0.15 |
-0.05 |
0.05 |
| pr04 |
4 |
495 |
2.84 |
1.04 |
3 |
2.87 |
1.48 |
0 |
5 |
5 |
-0.18 |
-0.04 |
0.05 |
| pr05 |
5 |
495 |
2.22 |
1.20 |
2 |
2.22 |
1.48 |
0 |
5 |
5 |
0.03 |
-0.31 |
0.05 |
| pr06 |
6 |
495 |
3.04 |
1.07 |
3 |
3.06 |
1.48 |
0 |
5 |
5 |
-0.26 |
0.07 |
0.05 |
| pr07 |
7 |
495 |
2.59 |
1.11 |
3 |
2.61 |
1.48 |
0 |
5 |
5 |
-0.15 |
-0.10 |
0.05 |
| pr08 |
8 |
495 |
3.05 |
0.91 |
3 |
3.09 |
0.00 |
0 |
5 |
5 |
-0.56 |
1.26 |
0.04 |
| pr09 |
9 |
495 |
2.89 |
0.95 |
3 |
2.94 |
0.00 |
0 |
5 |
5 |
-0.50 |
1.05 |
0.04 |
| pr10 |
10 |
495 |
2.83 |
1.04 |
3 |
2.90 |
1.48 |
0 |
5 |
5 |
-0.41 |
0.28 |
0.05 |
taia %>% select(all_of(pr_items)) %>%
pivot_longer(cols = all_of(pr_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkred") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Predictability") +
theme(plot.title = element_text(hjust = .5))

pr01 OK
- Я считаю, что интеллектуальные системы надежны
pr02 OK
- Я считаю, что результаты работы интеллектуальных систем хорошо предсказуемы
pr03 OK
- Я считаю, что интеллектуальные системы ненадежны (R)
pr04 OK
- Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
pr05 positive skewness
- Я считаю, что поездки в машине, управляемой искусственным интеллектом, безопаснее, чем в обычной
pr06 OK
- Я считаю, что медицинская диагностика с применением интеллектуальных систем надеждее, чем без них
pr07 OK
- Я считаю, что финансовые операции под контролем искусственного интеллекта безопаснее, чем обычные
pr08 high kurtosis
- Я считаю, что интеллектуальные системы, обеспечивающие безопасность домов, хорошо справляются со своей задачей
pr09 high kurtosis
- Я считаю, что рекомендательные системы чаще всего правильно определяют предпочтения пользователей
pr10 OK
taia %>%
select(all_of(co_items)) %>%
describe() %>%
kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)
Table 2: Consistency
| co01 |
1 |
495 |
2.49 |
1.08 |
3 |
2.51 |
1.48 |
0 |
5 |
5 |
-0.17 |
0.13 |
0.05 |
| co02 |
2 |
495 |
2.51 |
1.04 |
3 |
2.53 |
1.48 |
0 |
5 |
5 |
-0.19 |
-0.06 |
0.05 |
| co03 |
3 |
495 |
2.86 |
1.02 |
3 |
2.92 |
1.48 |
0 |
5 |
5 |
-0.40 |
0.31 |
0.05 |
| co04 |
4 |
495 |
3.47 |
1.09 |
4 |
3.54 |
1.48 |
0 |
5 |
5 |
-0.57 |
0.32 |
0.05 |
| co05 |
5 |
495 |
2.20 |
1.11 |
2 |
2.17 |
1.48 |
0 |
5 |
5 |
0.10 |
-0.17 |
0.05 |
| co06 |
6 |
495 |
2.52 |
1.11 |
3 |
2.53 |
1.48 |
0 |
5 |
5 |
-0.13 |
-0.15 |
0.05 |
| co07 |
7 |
495 |
1.59 |
1.13 |
2 |
1.52 |
1.48 |
0 |
5 |
5 |
0.54 |
0.12 |
0.05 |
| co08 |
8 |
495 |
1.90 |
1.04 |
2 |
1.86 |
1.48 |
0 |
5 |
5 |
0.40 |
0.28 |
0.05 |
| co09 |
9 |
495 |
2.05 |
1.07 |
2 |
2.01 |
1.48 |
0 |
5 |
5 |
0.35 |
0.12 |
0.05 |
| co10 |
10 |
495 |
2.44 |
1.10 |
2 |
2.44 |
1.48 |
0 |
5 |
5 |
-0.04 |
-0.06 |
0.05 |
taia %>% select(all_of(co_items)) %>%
pivot_longer(cols = all_of(co_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "chocolate3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Consistency") +
theme(plot.title = element_text(hjust = .5))

co01 OK
- Интеллектуальные системы надежны, так как те системы, с которыми я сталкивался (сталкивалась), были надежными
co02 OK
- Я считаю, что если система при тестировании работает корректно, то и дальше она будет работать корректно
co03 high kurtosis
- Я считаю, что интеллектуальные системы совершают меньше технических ошибок, чем люди
co04 high negative skewness
- Со временем любая интеллектуальная система будет совершать всё меньше ошибок
co05 positive skewness
- Если интеллектуальные системы, с которыми я сталкивался (сталкивалась), были надежными, то я могу считать надёжными другие системы
co06 positive skewness
- Я могу судить о надёжности новой интеллектуальной системы по опыту работы с другими системами
co07 high positive skewness
- Я могу сказать, что система работает корректно, только протестировав её (R)
co08 positive skewness
- Если одна интеллектуальная система подводит меня, то и с другими будет то же самое
co09positive skewness
- Если одна интеллектуальная система соответствует моим ожиданиям, то с другими будет то же самое
co10 positive skewness
- Я могу сформировать ожидания относительно работы интеллектуальных систем в целом на основе опыта взаимодействия с одной системой
taia %>%
select(all_of(ut_items)) %>%
describe() %>%
kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)
Table 3: Utility
| ut01 |
1 |
495 |
3.78 |
1.05 |
4 |
3.88 |
1.48 |
0 |
5 |
5 |
-0.86 |
1.12 |
0.05 |
| ut02 |
2 |
495 |
3.52 |
1.05 |
3 |
3.59 |
1.48 |
0 |
5 |
5 |
-0.53 |
0.50 |
0.05 |
| ut03 |
3 |
495 |
3.56 |
1.11 |
4 |
3.64 |
1.48 |
0 |
5 |
5 |
-0.57 |
0.15 |
0.05 |
| ut04 |
4 |
495 |
3.09 |
1.11 |
3 |
3.15 |
1.48 |
0 |
5 |
5 |
-0.43 |
0.03 |
0.05 |
| ut05 |
5 |
495 |
3.05 |
1.21 |
3 |
3.09 |
1.48 |
0 |
5 |
5 |
-0.33 |
-0.15 |
0.05 |
| ut06 |
6 |
495 |
3.27 |
1.10 |
3 |
3.31 |
1.48 |
0 |
5 |
5 |
-0.61 |
0.68 |
0.05 |
| ut07 |
7 |
495 |
3.20 |
1.13 |
3 |
3.23 |
1.48 |
0 |
5 |
5 |
-0.28 |
-0.22 |
0.05 |
| ut08 |
8 |
495 |
3.44 |
1.05 |
3 |
3.49 |
1.48 |
0 |
5 |
5 |
-0.55 |
0.44 |
0.05 |
| ut09 |
9 |
495 |
3.18 |
1.17 |
3 |
3.23 |
1.48 |
0 |
5 |
5 |
-0.47 |
0.16 |
0.05 |
| ut10 |
10 |
495 |
2.17 |
1.11 |
2 |
2.16 |
1.48 |
0 |
5 |
5 |
0.08 |
-0.23 |
0.05 |
| ut11 |
11 |
495 |
2.67 |
1.23 |
3 |
2.69 |
1.48 |
0 |
5 |
5 |
-0.11 |
-0.41 |
0.06 |
| ut12 |
12 |
495 |
3.16 |
1.15 |
3 |
3.21 |
1.48 |
0 |
5 |
5 |
-0.42 |
0.03 |
0.05 |
taia %>% select(all_of(ut_items)) %>%
pivot_longer(cols = all_of(ut_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "goldenrod3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Utility") +
theme(plot.title = element_text(hjust = .5))

ut01 extremely high negative skewness
- Я считаю, что интеллектуальные технологии — неотъемлемая часть развития общества
ut02 high negative skewness
- Я считаю, что человечество нуждается в интеллектуальных системах
ut03 high negative skewness
- Мне кажется, человечеству будет лучше без интеллектуальных систем (R)
ut04 negative skewness
- Я считаю, что развитие интеллектуальных систем — это оправданный риск для общества
ut05 OK
- По моему мнению, без интеллектуальных систем общественный прогресс остановился бы
ut06 negative skewness
- Я считаю, что интеллектуальные системы существенно повышают качество медицинской диагностики
ut07 light negative skewness
- Мне кажется, что оформить услуги с цифровыми помощниками гораздо проще, чем без них
ut08 negative skewness
- Мне кажется, рекомендательные сервисы существенно сокращают время поиска нужной информации
ut09 negative skewness
- Я считаю, что если полиция будет использовать интеллектуальные системы, то количество раскрытых преступлений увеличится
ut10 positive skewness
- Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
ut11 OK
- Я считаю, что если на дорогах появятся машины, управляемые искусственным интеллектом, то число аварий снизится
ut12 negative skewness
- Я считаю, что использование интеллектуальных систем в обучении повышает качество образования
taia %>%
select(all_of(fa_items)) %>%
describe() %>%
kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)
Table 4: Faith
| fa01 |
1 |
495 |
2.42 |
1.10 |
2 |
2.42 |
1.48 |
0 |
5 |
5 |
-0.02 |
-0.29 |
0.05 |
| fa02 |
2 |
495 |
2.16 |
1.18 |
2 |
2.15 |
1.48 |
0 |
5 |
5 |
0.18 |
-0.42 |
0.05 |
| fa03 |
3 |
495 |
1.51 |
1.13 |
1 |
1.42 |
1.48 |
0 |
5 |
5 |
0.66 |
0.16 |
0.05 |
| fa04 |
4 |
495 |
1.57 |
1.08 |
1 |
1.51 |
1.48 |
0 |
5 |
5 |
0.55 |
0.17 |
0.05 |
| fa05 |
5 |
495 |
2.46 |
1.10 |
2 |
2.48 |
1.48 |
0 |
5 |
5 |
-0.15 |
-0.10 |
0.05 |
| fa06 |
6 |
495 |
2.47 |
1.08 |
3 |
2.49 |
1.48 |
0 |
5 |
5 |
-0.18 |
0.06 |
0.05 |
| fa07 |
7 |
495 |
2.37 |
1.09 |
2 |
2.38 |
1.48 |
0 |
5 |
5 |
-0.14 |
-0.17 |
0.05 |
| fa08 |
8 |
495 |
2.21 |
1.14 |
2 |
2.17 |
1.48 |
0 |
5 |
5 |
0.28 |
-0.13 |
0.05 |
| fa09 |
9 |
495 |
2.29 |
1.18 |
2 |
2.27 |
1.48 |
0 |
5 |
5 |
0.15 |
-0.40 |
0.05 |
| fa10 |
10 |
495 |
2.64 |
1.20 |
3 |
2.62 |
1.48 |
0 |
5 |
5 |
0.06 |
-0.28 |
0.05 |
taia %>% select(all_of(fa_items)) %>%
pivot_longer(cols = all_of(fa_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkgreen") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Faith") +
theme(plot.title = element_text(hjust = .5))

fa01 OK
- Я готов (готова) довериться интеллектуальным системам, даже если я не до конца понимаю, как они работают
fa02 light positive skewness
- Чтобы доверять интеллектуальной системе, мне нужно точно понимать, как работают её алгоритмы (R)
fa03 hard positive skewness
- Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
fa04 hard positive skewness
- Если интеллектуальная система управлениями финансами не реагирует на запросы, я все равно буду уверен, что она работает корректно
fa05 OK
- Я готов (готова) доверять результатам работы интеллектуальных систем, даже если я не знаю, как они работают
fa06 OK
- Я готов (готова) довериться работе интеллектуальных систем так же, как работе профессионалов
fa07 OK
- Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
fa08 light positive skewness
- Мне нужно знать детали работы алгоритма, чтобы быть уверенным (уверенной) в качестве результата его работы (R)
fa09 OK
- Если я не понимаю, как работает интеллектуальная система, я не могу быть уверенным (уверенной) в результате её работы (R)
fa10 OK
- Мне не важно, как работает интеллектуальная система, если она должным образом справляется со своей задачей
taia %>%
select(all_of(de_items)) %>%
describe() %>%
kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)
Table 5: Dependability
| de01 |
1 |
495 |
2.59 |
1.10 |
3 |
2.64 |
1.48 |
0 |
5 |
5 |
-0.42 |
0.08 |
0.05 |
| de02 |
2 |
495 |
2.17 |
1.15 |
2 |
2.18 |
1.48 |
0 |
5 |
5 |
0.00 |
-0.34 |
0.05 |
| de03 |
3 |
495 |
2.17 |
1.19 |
2 |
2.17 |
1.48 |
0 |
5 |
5 |
0.04 |
-0.30 |
0.05 |
| de04 |
4 |
495 |
1.90 |
1.05 |
2 |
1.85 |
1.48 |
0 |
5 |
5 |
0.55 |
0.66 |
0.05 |
| de05 |
5 |
495 |
3.57 |
1.16 |
4 |
3.68 |
1.48 |
0 |
5 |
5 |
-0.78 |
0.48 |
0.05 |
| de06 |
6 |
495 |
2.23 |
1.23 |
2 |
2.25 |
1.48 |
0 |
5 |
5 |
0.01 |
-0.43 |
0.06 |
| de07 |
7 |
495 |
2.82 |
1.00 |
3 |
2.86 |
1.48 |
0 |
5 |
5 |
-0.30 |
0.31 |
0.04 |
| de08 |
8 |
495 |
2.65 |
1.06 |
3 |
2.70 |
1.48 |
0 |
5 |
5 |
-0.40 |
0.14 |
0.05 |
| de09 |
9 |
495 |
3.44 |
1.20 |
4 |
3.54 |
1.48 |
0 |
5 |
5 |
-0.61 |
-0.14 |
0.05 |
| de10 |
10 |
495 |
2.25 |
1.18 |
2 |
2.28 |
1.48 |
0 |
5 |
5 |
-0.22 |
-0.42 |
0.05 |
| de11 |
11 |
495 |
2.31 |
1.20 |
2 |
2.31 |
1.48 |
0 |
5 |
5 |
0.00 |
-0.52 |
0.05 |
taia %>% select(all_of(de_items)) %>%
pivot_longer(cols = all_of(de_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkblue") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Dependability") +
theme(plot.title = element_text(hjust = .5))

de01 negative skewness
- Я готов (готова) следовать рекомендациям рекомендательных систем социальных сетей
de02 OK
- Я готов (готова) делегировать управление финансами интеллектуальному помощнику
de03 OK
- Если интеллектуальная система запрашивает мои личные данные, я могу быть уверен (уверенна) в их сохранности
de04 hard positive skewness
- Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё
de05 hard negative skewness
- Я бы мог (могла) жить в «умном доме»
de06 negative kurtosis
- Если бы я ехал (ехала) в автомобиле, управляемом искусственным интеллектом, я бы был спокоен за свою безопасность
de07 OK
- Если меня обследуют с помощью интеллектуальных медицинских технологий, я могу доверять выставленному диагнозу
de08 OK
- Я считаю, что при покупках с интеллектуальными помощниками риск ошибиться меньше, чем без них
de09 negative skewness
- Мне было бы некомфортно жить в «умном доме» (R)
de10 negative kurtosis
- Я могу доверить искусственному интеллекту задачи, от которых зависит моя личная безопасность
de11 negative kurtosis
- Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)
taia %>%
select(all_of(un_items)) %>%
describe() %>%
kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)
Table 6: Understanding
| un01 |
1 |
495 |
2.93 |
1.05 |
3 |
3.01 |
1.48 |
0 |
5 |
5 |
-0.48 |
0.31 |
0.05 |
| un02 |
2 |
495 |
2.47 |
1.14 |
3 |
2.49 |
1.48 |
0 |
5 |
5 |
-0.17 |
-0.27 |
0.05 |
| un03 |
3 |
495 |
3.02 |
1.17 |
3 |
3.10 |
1.48 |
0 |
5 |
5 |
-0.55 |
0.01 |
0.05 |
| un04 |
4 |
495 |
2.61 |
1.09 |
3 |
2.65 |
1.48 |
0 |
5 |
5 |
-0.33 |
-0.22 |
0.05 |
| un05 |
5 |
495 |
2.82 |
1.10 |
3 |
2.90 |
1.48 |
0 |
5 |
5 |
-0.51 |
0.24 |
0.05 |
| un06 |
6 |
495 |
2.29 |
1.23 |
2 |
2.26 |
1.48 |
0 |
5 |
5 |
0.19 |
-0.62 |
0.06 |
| un07 |
7 |
495 |
2.13 |
1.18 |
2 |
2.14 |
1.48 |
0 |
5 |
5 |
-0.02 |
-0.54 |
0.05 |
| un08 |
8 |
495 |
2.90 |
1.16 |
3 |
2.96 |
1.48 |
0 |
5 |
5 |
-0.45 |
0.00 |
0.05 |
| un09 |
9 |
495 |
2.32 |
1.23 |
2 |
2.37 |
1.48 |
0 |
5 |
5 |
-0.18 |
-0.75 |
0.06 |
| un10 |
10 |
495 |
2.24 |
1.15 |
2 |
2.23 |
1.48 |
0 |
5 |
5 |
0.05 |
-0.44 |
0.05 |
| un11 |
11 |
495 |
2.63 |
1.20 |
3 |
2.66 |
1.48 |
0 |
5 |
5 |
-0.25 |
-0.37 |
0.05 |
| un12 |
12 |
495 |
2.89 |
1.12 |
3 |
2.96 |
1.48 |
0 |
5 |
5 |
-0.46 |
0.13 |
0.05 |
taia %>% select(all_of(un_items)) %>%
pivot_longer(cols = all_of(un_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "purple4") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Understanding") +
theme(plot.title = element_text(hjust = .5))

un01 OK
- Я понимаю, как происходит взаимодействие человека с интеллектуальными системами
un02 OK
- Я понимаю, как работают алгоритмы интеллектуальных систем
un03 negative skewness
- Я стараюсь разобраться в том, как работают интеллектуальные системы
un04 negative skewness
- Я понимаю, как работают отдельные элементы интеллектуальных систем
un05 positive kurtosis
- Я понимаю общий принцип работы интеллектуальных систем
un06 negative kurtosis
- Я плохо разбираюсь в тонкостях работы интеллектуальных систем (R)
un07 negative kurtosis
- Мне понятно, как работают интеллектуальные алгоритмы, применяемые в медицинской диагностике
un08 OK
- Мне понятно, как работают алгоритмы рекомендательных систем социальных сетей
un09 extra negative kurtosis
- Я понимаю, как работают алгоритмы автомобилей, управляемых искусственным интеллектом
un10 negative kurtosis
- Я понимаю, как работают алгоритмы интеллектуальных систем, которые управляют финансами
un11 OK
- Мне понятно, как устроены алгоритмы машинного перевода текстов
un12 OK
- Мне понятно, как работают алгоритмы систем типа «умный дом»
Correlations
Predictability
corrplot.mixed(cor(taia %>% select(all_of(pr_items))),
lower.col = "black")

Consistency
corrplot.mixed(cor(taia %>% select(all_of(co_items))),
lower.col = "black")

Utility
corrplot.mixed(cor(taia %>% select(all_of(ut_items))),
lower.col = "black")

Faith
corrplot.mixed(cor(taia %>% select(all_of(fa_items))),
lower.col = "black")

Dependability
corrplot.mixed(cor(taia %>% select(all_of(de_items))),
lower.col = "black")

Understanding
corrplot.mixed(cor(taia %>% select(all_of(un_items))),
lower.col = "black")

All TAIA items correlations
qgraph::qgraph(
cor(taia %>% select(all_of(taia_items))),
layout = "spring",
posCol = "darkgreen",
negCol = "darkred"
)

Psychometric Analysis
Subscales
Predictability
pr1 <- psych::alpha(
taia %>% select(all_of(pr_items)),
cumulative = TRUE,
title = "Predictability Factor",
check.keys = FALSE
)
kable(pr1$total,
caption = "Perdictability. Subscale statistics",
label = 7, digits = 2,
col.names = total_colnames
)
Table 7: Perdictability. Subscale statistics
|
0.81 |
0.81 |
0.82 |
0.3 |
4.25 |
0.01 |
27.92 |
6.23 |
0.33 |
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
caption = "Predictability. Items statistics",
label = 8, digits = 2,
col.names = item_stats_colnames)
Table 8: Predictability. Items statistics
| pr01 |
495 |
0.79 |
0.80 |
0.80 |
0.72 |
0.57 |
0.99 |
| pr02 |
495 |
0.66 |
0.66 |
0.62 |
0.56 |
0.55 |
0.97 |
| pr03 |
495 |
0.45 |
0.45 |
0.37 |
0.31 |
0.58 |
1.01 |
| pr04 |
495 |
0.32 |
0.32 |
0.21 |
0.16 |
0.57 |
1.04 |
| pr05 |
495 |
0.61 |
0.59 |
0.51 |
0.46 |
0.44 |
1.20 |
| pr06 |
495 |
0.62 |
0.62 |
0.56 |
0.50 |
0.61 |
1.07 |
| pr07 |
495 |
0.74 |
0.73 |
0.70 |
0.63 |
0.52 |
1.11 |
| pr08 |
495 |
0.72 |
0.73 |
0.71 |
0.64 |
0.61 |
0.91 |
| pr09 |
495 |
0.61 |
0.62 |
0.56 |
0.50 |
0.58 |
0.95 |
| pr10 |
495 |
0.55 |
0.56 |
0.47 |
0.42 |
0.57 |
1.04 |
pr1$item.stats %>%
ggplot(aes(x = row.names(pr1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Predictability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(pr1$alpha.drop,
caption = "Predictability. Subscale statistics when item drop",
label = 9, digits = 2, col.names = alpha_drop_colnames)
Table 9: Predictability. Subscale statistics when item drop
| pr01 |
0.76 |
0.77 |
0.78 |
0.27 |
3.27 |
0.02 |
0.02 |
0.29 |
| pr02 |
0.78 |
0.79 |
0.80 |
0.29 |
3.66 |
0.01 |
0.03 |
0.32 |
| pr03 |
0.81 |
0.81 |
0.81 |
0.32 |
4.33 |
0.01 |
0.03 |
0.36 |
| pr04 |
0.82 |
0.83 |
0.82 |
0.35 |
4.78 |
0.01 |
0.02 |
0.36 |
| pr05 |
0.79 |
0.80 |
0.81 |
0.30 |
3.88 |
0.01 |
0.03 |
0.33 |
| pr06 |
0.79 |
0.79 |
0.81 |
0.30 |
3.79 |
0.01 |
0.03 |
0.33 |
| pr07 |
0.77 |
0.78 |
0.79 |
0.28 |
3.46 |
0.02 |
0.02 |
0.30 |
| pr08 |
0.77 |
0.78 |
0.79 |
0.28 |
3.46 |
0.02 |
0.02 |
0.30 |
| pr09 |
0.79 |
0.79 |
0.80 |
0.30 |
3.80 |
0.01 |
0.03 |
0.35 |
| pr10 |
0.80 |
0.80 |
0.81 |
0.31 |
3.99 |
0.01 |
0.03 |
0.35 |
kable(pr1$response.freq,
caption = "Predictability. Non missing response frequency for each item",
label = 10, digits = 2)
Table 10: Predictability. Non missing response frequency for each item
| pr01 |
0.02 |
0.06 |
0.24 |
0.45 |
0.19 |
0.04 |
0 |
| pr02 |
0.01 |
0.08 |
0.28 |
0.43 |
0.17 |
0.03 |
0 |
| pr03 |
0.01 |
0.06 |
0.26 |
0.40 |
0.22 |
0.05 |
0 |
| pr04 |
0.02 |
0.07 |
0.27 |
0.38 |
0.21 |
0.05 |
0 |
| pr05 |
0.09 |
0.17 |
0.33 |
0.29 |
0.09 |
0.03 |
0 |
| pr06 |
0.02 |
0.06 |
0.20 |
0.41 |
0.23 |
0.08 |
0 |
| pr07 |
0.04 |
0.12 |
0.28 |
0.38 |
0.14 |
0.04 |
0 |
| pr08 |
0.02 |
0.03 |
0.16 |
0.52 |
0.24 |
0.04 |
0 |
| pr09 |
0.02 |
0.05 |
0.18 |
0.53 |
0.17 |
0.04 |
0 |
| pr10 |
0.02 |
0.08 |
0.21 |
0.45 |
0.20 |
0.04 |
0 |
Consistency
co1 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)
Some items ( co07 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
kable(co1$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)
Table 11: Consistency. Subscale statistics
|
0.76 |
0.77 |
0.8 |
0.25 |
3.27 |
0.01 |
24.03 |
6.1 |
0.27 |
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)
Table 12: Consistency. Items statistics
| co01 |
495 |
0.74 |
0.74 |
0.72 |
0.64 |
0.50 |
1.08 |
| co02 |
495 |
0.68 |
0.69 |
0.65 |
0.57 |
0.50 |
1.04 |
| co03 |
495 |
0.55 |
0.55 |
0.48 |
0.41 |
0.57 |
1.02 |
| co04 |
495 |
0.40 |
0.41 |
0.30 |
0.24 |
0.69 |
1.09 |
| co05 |
495 |
0.79 |
0.78 |
0.79 |
0.70 |
0.44 |
1.11 |
| co06 |
495 |
0.65 |
0.65 |
0.61 |
0.53 |
0.50 |
1.11 |
| co07 |
495 |
-0.03 |
-0.04 |
-0.22 |
-0.22 |
0.32 |
1.13 |
| co08 |
495 |
0.45 |
0.46 |
0.36 |
0.30 |
0.38 |
1.04 |
| co09 |
495 |
0.76 |
0.77 |
0.76 |
0.67 |
0.41 |
1.07 |
| co10 |
495 |
0.67 |
0.67 |
0.62 |
0.56 |
0.49 |
1.10 |
co1$item.stats %>%
ggplot(aes(x = row.names(co1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(co1$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)
Table 13: Consistency. Subscale statistics when item drop
| co01 |
0.71 |
0.72 |
0.76 |
0.22 |
2.52 |
0.02 |
0.06 |
0.26 |
| co02 |
0.72 |
0.73 |
0.77 |
0.23 |
2.66 |
0.02 |
0.06 |
0.26 |
| co03 |
0.74 |
0.75 |
0.79 |
0.25 |
2.98 |
0.02 |
0.07 |
0.27 |
| co04 |
0.77 |
0.77 |
0.80 |
0.27 |
3.36 |
0.01 |
0.06 |
0.35 |
| co05 |
0.70 |
0.71 |
0.75 |
0.21 |
2.42 |
0.02 |
0.06 |
0.26 |
| co06 |
0.73 |
0.73 |
0.77 |
0.23 |
2.74 |
0.02 |
0.06 |
0.27 |
| co07 |
0.83 |
0.82 |
0.83 |
0.34 |
4.70 |
0.01 |
0.03 |
0.36 |
| co08 |
0.76 |
0.76 |
0.79 |
0.26 |
3.23 |
0.02 |
0.07 |
0.34 |
| co09 |
0.71 |
0.71 |
0.75 |
0.22 |
2.47 |
0.02 |
0.06 |
0.26 |
| co10 |
0.72 |
0.73 |
0.77 |
0.23 |
2.69 |
0.02 |
0.07 |
0.26 |
kable(co1$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)
Table 14: Consistency. Non missing response frequency for each item
| co01 |
0.05 |
0.11 |
0.31 |
0.39 |
0.11 |
0.03 |
0 |
| co02 |
0.03 |
0.12 |
0.32 |
0.38 |
0.13 |
0.02 |
0 |
| co03 |
0.02 |
0.07 |
0.22 |
0.44 |
0.21 |
0.04 |
0 |
| co04 |
0.01 |
0.04 |
0.10 |
0.35 |
0.32 |
0.18 |
0 |
| co05 |
0.06 |
0.19 |
0.37 |
0.27 |
0.09 |
0.02 |
0 |
| co06 |
0.04 |
0.14 |
0.28 |
0.38 |
0.13 |
0.03 |
0 |
| co07 |
0.18 |
0.31 |
0.32 |
0.14 |
0.04 |
0.02 |
0 |
| co08 |
0.08 |
0.27 |
0.42 |
0.16 |
0.06 |
0.01 |
0 |
| co09 |
0.06 |
0.24 |
0.40 |
0.22 |
0.06 |
0.02 |
0 |
| co10 |
0.04 |
0.14 |
0.33 |
0.34 |
0.11 |
0.03 |
0 |
Utility
ut1 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)
kable(ut1$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)
Table 15: Utility. Subscale statistics
|
0.86 |
0.86 |
0.87 |
0.34 |
6.17 |
0.01 |
38.09 |
8.44 |
0.37 |
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)
Table 16: Utility. Items statistics
| ut01 |
495 |
0.77 |
0.78 |
0.78 |
0.71 |
0.76 |
1.05 |
| ut02 |
495 |
0.82 |
0.83 |
0.84 |
0.77 |
0.70 |
1.05 |
| ut03 |
495 |
0.57 |
0.57 |
0.52 |
0.47 |
0.71 |
1.11 |
| ut04 |
495 |
0.51 |
0.51 |
0.44 |
0.40 |
0.62 |
1.11 |
| ut05 |
495 |
0.69 |
0.69 |
0.65 |
0.61 |
0.61 |
1.21 |
| ut06 |
495 |
0.76 |
0.76 |
0.74 |
0.70 |
0.65 |
1.10 |
| ut07 |
495 |
0.63 |
0.63 |
0.59 |
0.54 |
0.64 |
1.13 |
| ut08 |
495 |
0.65 |
0.66 |
0.62 |
0.57 |
0.69 |
1.05 |
| ut09 |
495 |
0.68 |
0.68 |
0.64 |
0.59 |
0.64 |
1.17 |
| ut10 |
495 |
0.17 |
0.17 |
0.06 |
0.04 |
0.43 |
1.11 |
| ut11 |
495 |
0.56 |
0.55 |
0.48 |
0.45 |
0.53 |
1.23 |
| ut12 |
495 |
0.71 |
0.71 |
0.68 |
0.64 |
0.63 |
1.15 |
ut1$item.stats %>%
ggplot(aes(x = row.names(ut1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(ut1$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)
Table 17: Utility. Subscale statistics when item drop
| ut01 |
0.84 |
0.84 |
0.85 |
0.32 |
5.16 |
0.01 |
0.03 |
0.34 |
| ut02 |
0.83 |
0.83 |
0.84 |
0.31 |
5.00 |
0.01 |
0.03 |
0.33 |
| ut03 |
0.85 |
0.85 |
0.86 |
0.35 |
5.84 |
0.01 |
0.04 |
0.41 |
| ut04 |
0.86 |
0.86 |
0.87 |
0.36 |
6.06 |
0.01 |
0.03 |
0.41 |
| ut05 |
0.84 |
0.84 |
0.86 |
0.33 |
5.45 |
0.01 |
0.04 |
0.37 |
| ut06 |
0.84 |
0.84 |
0.85 |
0.32 |
5.21 |
0.01 |
0.03 |
0.33 |
| ut07 |
0.85 |
0.85 |
0.86 |
0.34 |
5.65 |
0.01 |
0.04 |
0.37 |
| ut08 |
0.85 |
0.85 |
0.86 |
0.34 |
5.55 |
0.01 |
0.03 |
0.37 |
| ut09 |
0.84 |
0.85 |
0.86 |
0.33 |
5.49 |
0.01 |
0.03 |
0.37 |
| ut10 |
0.88 |
0.88 |
0.88 |
0.40 |
7.40 |
0.01 |
0.01 |
0.41 |
| ut11 |
0.85 |
0.86 |
0.87 |
0.35 |
5.92 |
0.01 |
0.04 |
0.41 |
| ut12 |
0.84 |
0.84 |
0.86 |
0.33 |
5.37 |
0.01 |
0.03 |
0.35 |
kable(ut1$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)
Table 18: Utility. Non missing response frequency for each item
| ut01 |
0.01 |
0.01 |
0.06 |
0.29 |
0.34 |
0.28 |
0 |
| ut02 |
0.01 |
0.02 |
0.09 |
0.38 |
0.30 |
0.20 |
0 |
| ut03 |
0.01 |
0.04 |
0.09 |
0.33 |
0.31 |
0.22 |
0 |
| ut04 |
0.02 |
0.08 |
0.15 |
0.40 |
0.27 |
0.09 |
0 |
| ut05 |
0.03 |
0.06 |
0.20 |
0.35 |
0.23 |
0.12 |
0 |
| ut06 |
0.03 |
0.03 |
0.13 |
0.39 |
0.30 |
0.12 |
0 |
| ut07 |
0.01 |
0.05 |
0.19 |
0.35 |
0.26 |
0.14 |
0 |
| ut08 |
0.01 |
0.03 |
0.11 |
0.36 |
0.34 |
0.15 |
0 |
| ut09 |
0.03 |
0.05 |
0.15 |
0.38 |
0.25 |
0.13 |
0 |
| ut10 |
0.07 |
0.19 |
0.37 |
0.26 |
0.09 |
0.02 |
0 |
| ut11 |
0.05 |
0.12 |
0.27 |
0.32 |
0.18 |
0.07 |
0 |
| ut12 |
0.02 |
0.07 |
0.15 |
0.38 |
0.26 |
0.12 |
0 |
Faith
fa1 <- psych::alpha(
taia %>% select(all_of(fa_items)),
cumulative = TRUE,
title = "Faith Factor",
check.keys = FALSE
)
kable(fa1$total,
caption = "Faith. Subscale statistics",
label = 19, digits = 2,
col.names = total_colnames)
Table 19: Faith. Subscale statistics
|
0.77 |
0.77 |
0.81 |
0.25 |
3.26 |
0.02 |
22.1 |
6.41 |
0.24 |
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
caption = "Faith. Items statistics",
label = 20, digits = 2,
col.names = item_stats_colnames)
Table 20: Faith. Items statistics
| fa01 |
495 |
0.76 |
0.76 |
0.76 |
0.67 |
0.48 |
1.10 |
| fa02 |
495 |
0.61 |
0.60 |
0.56 |
0.47 |
0.43 |
1.18 |
| fa03 |
495 |
0.27 |
0.28 |
0.16 |
0.10 |
0.30 |
1.13 |
| fa04 |
495 |
0.41 |
0.42 |
0.33 |
0.26 |
0.31 |
1.08 |
| fa05 |
495 |
0.77 |
0.78 |
0.78 |
0.69 |
0.49 |
1.10 |
| fa06 |
495 |
0.55 |
0.56 |
0.50 |
0.42 |
0.49 |
1.08 |
| fa07 |
495 |
0.42 |
0.42 |
0.31 |
0.26 |
0.47 |
1.09 |
| fa08 |
495 |
0.58 |
0.57 |
0.52 |
0.44 |
0.44 |
1.14 |
| fa09 |
495 |
0.66 |
0.65 |
0.62 |
0.53 |
0.46 |
1.18 |
| fa10 |
495 |
0.63 |
0.62 |
0.57 |
0.50 |
0.53 |
1.20 |
fa1$item.stats %>%
ggplot(aes(x = row.names(fa1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Faith. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(fa1$alpha.drop,
caption = "Faith. Subscale statistics when item drop",
label = 21, digits = 2,
col.names = alpha_drop_colnames)
Table 21: Faith. Subscale statistics when item drop
| fa01 |
0.71 |
0.71 |
0.76 |
0.22 |
2.47 |
0.02 |
0.04 |
0.22 |
| fa02 |
0.74 |
0.74 |
0.78 |
0.24 |
2.87 |
0.02 |
0.04 |
0.24 |
| fa03 |
0.79 |
0.79 |
0.82 |
0.29 |
3.70 |
0.01 |
0.04 |
0.30 |
| fa04 |
0.77 |
0.77 |
0.81 |
0.27 |
3.30 |
0.02 |
0.04 |
0.26 |
| fa05 |
0.71 |
0.71 |
0.76 |
0.21 |
2.43 |
0.02 |
0.04 |
0.22 |
| fa06 |
0.75 |
0.75 |
0.79 |
0.25 |
2.95 |
0.02 |
0.05 |
0.29 |
| fa07 |
0.77 |
0.77 |
0.81 |
0.27 |
3.30 |
0.02 |
0.05 |
0.30 |
| fa08 |
0.74 |
0.75 |
0.79 |
0.25 |
2.92 |
0.02 |
0.04 |
0.24 |
| fa09 |
0.73 |
0.73 |
0.78 |
0.23 |
2.73 |
0.02 |
0.04 |
0.24 |
| fa10 |
0.74 |
0.74 |
0.79 |
0.24 |
2.79 |
0.02 |
0.04 |
0.24 |
kable(fa1$response.freq,
caption = "Faith. Non missing response frequency for each item",
label = 22, digits = 2)
Table 22: Faith. Non missing response frequency for each item
| fa01 |
0.04 |
0.16 |
0.32 |
0.33 |
0.13 |
0.03 |
0 |
| fa02 |
0.08 |
0.21 |
0.36 |
0.21 |
0.12 |
0.02 |
0 |
| fa03 |
0.19 |
0.35 |
0.29 |
0.11 |
0.05 |
0.01 |
0 |
| fa04 |
0.16 |
0.34 |
0.33 |
0.12 |
0.04 |
0.01 |
0 |
| fa05 |
0.05 |
0.12 |
0.33 |
0.34 |
0.13 |
0.03 |
0 |
| fa06 |
0.05 |
0.12 |
0.32 |
0.38 |
0.11 |
0.03 |
0 |
| fa07 |
0.05 |
0.15 |
0.32 |
0.35 |
0.11 |
0.02 |
0 |
| fa08 |
0.05 |
0.20 |
0.38 |
0.23 |
0.10 |
0.03 |
0 |
| fa09 |
0.06 |
0.19 |
0.34 |
0.25 |
0.13 |
0.03 |
0 |
| fa10 |
0.04 |
0.12 |
0.31 |
0.32 |
0.14 |
0.08 |
0 |
Dependability
de1 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)
Some items ( de04 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
kable(de1$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)
Table 23: Dependability. Subscale statistics
|
0.75 |
0.75 |
0.8 |
0.21 |
2.96 |
0.02 |
28.09 |
6.74 |
0.24 |
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)
Table 24: Dependability. Items statistics
| de01 |
495 |
0.57 |
0.58 |
0.52 |
0.45 |
0.52 |
1.10 |
| de02 |
495 |
0.72 |
0.72 |
0.69 |
0.61 |
0.43 |
1.15 |
| de03 |
495 |
0.66 |
0.66 |
0.61 |
0.54 |
0.43 |
1.19 |
| de04 |
495 |
-0.23 |
-0.22 |
-0.39 |
-0.36 |
0.38 |
1.05 |
| de05 |
495 |
0.59 |
0.58 |
0.56 |
0.46 |
0.71 |
1.16 |
| de06 |
495 |
0.67 |
0.67 |
0.62 |
0.55 |
0.45 |
1.23 |
| de07 |
495 |
0.58 |
0.60 |
0.54 |
0.47 |
0.56 |
1.00 |
| de08 |
495 |
0.67 |
0.68 |
0.66 |
0.57 |
0.53 |
1.06 |
| de09 |
495 |
0.45 |
0.44 |
0.39 |
0.30 |
0.69 |
1.20 |
| de10 |
495 |
0.74 |
0.74 |
0.72 |
0.64 |
0.45 |
1.18 |
| de11 |
495 |
0.43 |
0.42 |
0.30 |
0.27 |
0.46 |
1.20 |
de1$item.stats %>%
ggplot(aes(x = row.names(de1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(de1$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)
Table 25: Dependability. Subscale statistics when item drop
| de01 |
0.73 |
0.72 |
0.78 |
0.21 |
2.59 |
0.02 |
0.07 |
0.22 |
| de02 |
0.71 |
0.70 |
0.76 |
0.19 |
2.32 |
0.02 |
0.07 |
0.22 |
| de03 |
0.72 |
0.71 |
0.77 |
0.20 |
2.44 |
0.02 |
0.07 |
0.23 |
| de04 |
0.82 |
0.82 |
0.84 |
0.31 |
4.49 |
0.01 |
0.02 |
0.32 |
| de05 |
0.73 |
0.72 |
0.76 |
0.21 |
2.59 |
0.02 |
0.07 |
0.22 |
| de06 |
0.71 |
0.71 |
0.77 |
0.19 |
2.42 |
0.02 |
0.07 |
0.22 |
| de07 |
0.73 |
0.72 |
0.78 |
0.20 |
2.56 |
0.02 |
0.07 |
0.22 |
| de08 |
0.71 |
0.70 |
0.76 |
0.19 |
2.39 |
0.02 |
0.06 |
0.22 |
| de09 |
0.75 |
0.74 |
0.78 |
0.22 |
2.88 |
0.02 |
0.07 |
0.30 |
| de10 |
0.70 |
0.69 |
0.76 |
0.19 |
2.28 |
0.02 |
0.06 |
0.22 |
| de11 |
0.75 |
0.75 |
0.80 |
0.23 |
2.93 |
0.02 |
0.08 |
0.32 |
kable(de1$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)
Table 26: Dependability. Non missing response frequency for each item
| de01 |
0.05 |
0.11 |
0.24 |
0.43 |
0.14 |
0.03 |
0 |
| de02 |
0.09 |
0.17 |
0.36 |
0.26 |
0.10 |
0.02 |
0 |
| de03 |
0.10 |
0.17 |
0.35 |
0.27 |
0.09 |
0.03 |
0 |
| de04 |
0.07 |
0.26 |
0.45 |
0.14 |
0.05 |
0.02 |
0 |
| de05 |
0.02 |
0.03 |
0.10 |
0.28 |
0.34 |
0.23 |
0 |
| de06 |
0.10 |
0.17 |
0.32 |
0.28 |
0.11 |
0.03 |
0 |
| de07 |
0.02 |
0.06 |
0.27 |
0.42 |
0.20 |
0.03 |
0 |
| de08 |
0.04 |
0.10 |
0.24 |
0.44 |
0.15 |
0.03 |
0 |
| de09 |
0.01 |
0.06 |
0.13 |
0.27 |
0.33 |
0.20 |
0 |
| de10 |
0.10 |
0.15 |
0.30 |
0.34 |
0.10 |
0.02 |
0 |
| de11 |
0.07 |
0.20 |
0.28 |
0.31 |
0.12 |
0.03 |
0 |
Understanding
un1 <- psych::alpha(
taia %>% select(all_of(un_items)),
cumulative = TRUE,
title = "Understanding Factor",
check.keys = FALSE
)
kable(un1$total,
caption = "Understanding. Subscale statistics",
label = 27, digits = 2,
col.names = total_colnames)
Table 27: Understanding. Subscale statistics
|
0.92 |
0.92 |
0.92 |
0.5 |
12 |
0.01 |
31.24 |
10.15 |
0.51 |
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
caption = "Understanding. Items statistics",
label = 28, digits = 2,
col.names = item_stats_colnames)
Table 28: Understanding. Items statistics
| un01 |
495 |
0.75 |
0.75 |
0.73 |
0.70 |
0.59 |
1.05 |
| un02 |
495 |
0.84 |
0.84 |
0.84 |
0.81 |
0.49 |
1.14 |
| un03 |
495 |
0.59 |
0.59 |
0.54 |
0.51 |
0.60 |
1.17 |
| un04 |
495 |
0.76 |
0.76 |
0.73 |
0.71 |
0.52 |
1.09 |
| un05 |
495 |
0.81 |
0.81 |
0.80 |
0.77 |
0.56 |
1.10 |
| un06 |
495 |
0.57 |
0.56 |
0.50 |
0.48 |
0.46 |
1.23 |
| un07 |
495 |
0.72 |
0.72 |
0.69 |
0.66 |
0.43 |
1.18 |
| un08 |
495 |
0.76 |
0.76 |
0.75 |
0.71 |
0.58 |
1.16 |
| un09 |
495 |
0.73 |
0.73 |
0.69 |
0.67 |
0.46 |
1.23 |
| un10 |
495 |
0.75 |
0.75 |
0.72 |
0.69 |
0.45 |
1.15 |
| un11 |
495 |
0.79 |
0.79 |
0.77 |
0.74 |
0.53 |
1.20 |
| un12 |
495 |
0.75 |
0.76 |
0.73 |
0.70 |
0.58 |
1.12 |
un1$item.stats %>%
ggplot(aes(x = row.names(un1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Understanding. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(un1$alpha.drop,
caption = "Understanding. Subscale statistics when item drop",
label = 29, digits = 2,
col.names = alpha_drop_colnames)
Table 29: Understanding. Subscale statistics when item drop
| un01 |
0.91 |
0.92 |
0.91 |
0.50 |
10.87 |
0.01 |
0.01 |
0.51 |
| un02 |
0.91 |
0.91 |
0.91 |
0.48 |
10.26 |
0.01 |
0.01 |
0.50 |
| un03 |
0.92 |
0.92 |
0.92 |
0.52 |
12.05 |
0.01 |
0.01 |
0.54 |
| un04 |
0.91 |
0.92 |
0.92 |
0.50 |
10.82 |
0.01 |
0.01 |
0.51 |
| un05 |
0.91 |
0.91 |
0.91 |
0.49 |
10.46 |
0.01 |
0.01 |
0.50 |
| un06 |
0.92 |
0.92 |
0.92 |
0.53 |
12.30 |
0.01 |
0.01 |
0.54 |
| un07 |
0.92 |
0.92 |
0.92 |
0.50 |
11.10 |
0.01 |
0.01 |
0.51 |
| un08 |
0.91 |
0.92 |
0.91 |
0.50 |
10.80 |
0.01 |
0.01 |
0.51 |
| un09 |
0.92 |
0.92 |
0.92 |
0.50 |
11.06 |
0.01 |
0.01 |
0.51 |
| un10 |
0.91 |
0.92 |
0.92 |
0.50 |
10.93 |
0.01 |
0.01 |
0.51 |
| un11 |
0.91 |
0.91 |
0.91 |
0.49 |
10.62 |
0.01 |
0.01 |
0.50 |
| un12 |
0.91 |
0.92 |
0.92 |
0.50 |
10.86 |
0.01 |
0.01 |
0.50 |
kable(un1$response.freq,
caption = "Understanding. Non missing response frequency for each item",
label = 30, digits = 2)
Table 30: Understanding. Non missing response frequency for each item
| un01 |
0.02 |
0.07 |
0.19 |
0.43 |
0.24 |
0.05 |
0 |
| un02 |
0.05 |
0.14 |
0.29 |
0.35 |
0.14 |
0.03 |
0 |
| un03 |
0.03 |
0.08 |
0.16 |
0.36 |
0.29 |
0.07 |
0 |
| un04 |
0.03 |
0.13 |
0.24 |
0.40 |
0.17 |
0.02 |
0 |
| un05 |
0.04 |
0.08 |
0.19 |
0.44 |
0.21 |
0.04 |
0 |
| un06 |
0.06 |
0.24 |
0.28 |
0.25 |
0.14 |
0.04 |
0 |
| un07 |
0.09 |
0.21 |
0.30 |
0.29 |
0.09 |
0.02 |
0 |
| un08 |
0.04 |
0.09 |
0.18 |
0.39 |
0.23 |
0.06 |
0 |
| un09 |
0.08 |
0.18 |
0.25 |
0.30 |
0.17 |
0.01 |
0 |
| un10 |
0.06 |
0.20 |
0.33 |
0.27 |
0.12 |
0.02 |
0 |
| un11 |
0.05 |
0.13 |
0.23 |
0.36 |
0.18 |
0.05 |
0 |
| un12 |
0.03 |
0.08 |
0.19 |
0.41 |
0.23 |
0.06 |
0 |
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)

Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.94
G.6: 0.97
Omega Hierarchical: 0.6
Omega H asymptotic: 0.62
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* F4* F5* F6* h2 u2 p2
pr01 0.57 0.34 0.26 0.57 0.43 0.58
pr02 0.45 0.32 0.68 0.65
pr03 0.20 0.54 0.39 0.61 0.10
pr04 0.43 0.24 0.76 0.02
pr05 0.56 0.45 0.52 0.48 0.60
pr06 0.43 0.37 0.35 0.65 0.53
pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
pr08 0.52 0.38 0.23 0.49 0.51 0.54
pr09 0.41 0.29 0.29 0.71 0.57
pr10 0.37 0.25 0.23 0.77 0.58
co01 0.51 0.51 0.54 0.46 0.48
co02 0.44 0.55 0.49 0.51 0.39
co03 0.47 0.29 0.37 0.63 0.60
co04 0.33 0.44 0.38 0.62 0.28
co05 0.44 0.63 0.60 0.40 0.33
co06 0.37 0.51 0.40 0.60 0.34
co07- 0.29 0.17 0.83 0.11
co08 0.34 -0.37 0.41 0.59 0.02
co09 0.40 0.60 0.54 0.46 0.29
co10 0.38 0.45 0.40 0.60 0.36
ut01 0.36 0.67 0.61 0.39 0.21
ut02 0.45 0.63 0.64 0.36 0.31
ut03 0.22 0.32 0.52 0.54 0.46 0.09
ut04 0.27 0.39 0.24 0.76 0.31
ut05 0.41 0.44 0.37 0.63 0.45
ut06 0.46 0.60 0.56 0.44 0.38
ut07 0.34 0.46 0.34 0.66 0.35
ut08 0.40 0.47 0.40 0.60 0.39
ut09 0.42 0.46 0.39 0.61 0.44
ut10 0.24 0.09 0.91 0.03
ut11 0.54 0.21 0.39 0.48 0.52 0.61
ut12 0.46 0.47 0.45 0.55 0.47
fa01 0.44 0.61 0.60 0.40 0.33
fa02 0.67 0.54 0.46 0.00
fa03 -0.37 0.31 0.69 0.12
fa04 0.35 0.32 0.21 0.40 0.60 0.32
fa05 0.47 0.60 0.62 0.38 0.36
fa06 0.58 0.30 0.52 0.48 0.65
fa07 0.23 0.20 0.47 0.36 0.64 0.14
fa08 0.61 0.49 0.51 0.00
fa09 0.64 0.23 0.55 0.45 0.02
fa10 0.24 0.63 0.48 0.52 0.12
de01 0.43 0.29 0.71 0.63
de02 0.57 0.23 0.30 0.49 0.51 0.67
de03 0.50 0.28 0.38 0.62 0.65
de04- 0.31 0.39 0.27 0.73 0.37
de05 0.38 0.41 0.29 0.45 0.55 0.32
de06 0.58 0.47 0.58 0.42 0.59
de07 0.49 0.34 0.40 0.60 0.59
de08 0.54 0.23 0.25 0.43 0.57 0.68
de09 0.22 0.59 0.43 0.57 0.12
de10 0.60 0.40 0.56 0.44 0.64
de11 0.22 0.24 0.32 0.27 0.73 0.18
un01 0.25 0.72 0.63 0.37 0.10
un02 0.28 0.79 0.70 0.30 0.11
un03 0.43 -0.26 0.36 0.64 0.10
un04 0.25 0.68 0.53 0.47 0.12
un05 0.28 0.77 0.67 0.33 0.11
un06 -0.29 0.51 0.32 0.40 0.60 0.02
un07 0.33 0.59 0.54 0.46 0.20
un08 0.24 0.73 0.60 0.40 0.10
un09 0.31 0.61 0.52 0.48 0.18
un10 0.27 0.65 0.57 0.43 0.13
un11 0.24 0.72 0.60 0.40 0.10
un12 0.26 0.68 0.57 0.43 0.12
With eigenvalues of:
g F1* F2* F3* F4* F5* F6*
9.4 4.6 5.6 2.8 2.7 1.6 2.3
general/max 1.68 max/min = 3.6
mean percent general = 0.32 with sd = 0.22 and cv of 0.68
Explained Common Variance of the general factor = 0.32
The degrees of freedom are 1705 and the fit is 8.03
The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.04
RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
BIC = -6825.22
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 2015 and the fit is 22.88
The number of observations was 495 with Chi Square = 10774.78 with prob < 0
The root mean square of the residuals is 0.15
The df corrected root mean square of the residuals is 0.15
RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
BIC = -1727.41
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.81 0.89 0.97 0.86
Multiple R square of scores with factors 0.66 0.79 0.94 0.74
Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47
F4* F5* F6*
Correlation of scores with factors 0.94 0.71 0.91
Multiple R square of scores with factors 0.88 0.50 0.83
Minimum correlation of factor score estimates 0.76 0.00 0.66
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.96 0.87 0.91 0.86
Omega general for total scores and subscales 0.60 0.43 0.11 0.45
Omega group for total scores and subscales 0.21 0.44 0.79 0.41
F4* F5* F6*
Omega total for total scores and subscales 0.83 0.81 0.51
Omega general for total scores and subscales 0.09 0.57 0.14
Omega group for total scores and subscales 0.74 0.24 0.37
splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")
Split half reliabilities
Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
key = NULL, use = "pairwise")
Maximum split half reliability (lambda 4) = 0.96
Guttman lambda 6 = 0.96
Average split half reliability = 0.93
Guttman lambda 3 (alpha) = 0.93
Guttman lambda 2 = 0.94
Minimum split half reliability (beta) = 0.88
Average interitem r = 0.17 with median = 0.18
guttman(taia %>% ungroup() %>% select(all_of(taia_items)))
Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
Alternative estimates of reliability
Guttman bounds
L1 = 0.92
L2 = 0.94
L3 (alpha) = 0.93
L4 (max) = 0.97
L5 = 0.92
L6 (smc) = 0.96
TenBerge bounds
mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
alpha of first PC = 0.95
estimated greatest lower bound based upon communalities= 0.97
beta found by splitHalf = 0.84
glb.fa(taia %>% ungroup() %>% select(all_of(taia_items)))
$glb
[1] 0.965817
$communality
pr01 pr02 pr03 pr04 pr05 pr06 pr07
0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103
pr08 pr09 pr10 co01 co02 co03 co04
0.6112839 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165
co05 co06 co07 co08 co09 co10 ut01
0.7521395 0.5726406 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201
ut02 ut03 ut04 ut05 ut06 ut07 ut08
0.7277348 0.6422830 0.3722353 0.5234489 0.7171434 0.5757501 0.5792722
ut09 ut10 ut11 ut12 fa01 fa02 fa03
0.7253062 0.3644927 0.6451266 0.5389414 0.6943597 0.5928412 0.4376811
fa04 fa05 fa06 fa07 fa08 fa09 fa10
0.5197275 0.7565165 0.5844505 0.4786126 0.6061162 0.6945327 0.4919291
de01 de02 de03 de04 de05 de06 de07
0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322 0.5278337
de08 de09 de10 de11 un01 un02 un03
0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
un04 un05 un06 un07 un08 un09 un10
0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091
un11 un12
0.6870705 0.6276157
$numf
[1] 19
$Call
glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))
Items exclusion
First step
Excluded items: co07, ut10, de04
Reason: negative discrimination
co_items_old <- co_items
co_items[-7] -> co_items
ut_items_old <- ut_items
ut_items[-10] -> ut_items
de_items_old <- de_items
de_items[-4] -> de_items
Subscales after first step of exclusion
Consistency
co2 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)
kable(co2$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)
Table 11: Consistency. Subscale statistics
|
0.83 |
0.82 |
0.83 |
0.34 |
4.7 |
0.01 |
22.44 |
6.24 |
0.36 |
co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)
Table 12: Consistency. Items statistics
| co01 |
495 |
0.74 |
0.74 |
0.71 |
0.65 |
0.50 |
1.08 |
| co02 |
495 |
0.72 |
0.72 |
0.67 |
0.62 |
0.50 |
1.04 |
| co03 |
495 |
0.56 |
0.57 |
0.48 |
0.43 |
0.57 |
1.02 |
| co04 |
495 |
0.44 |
0.43 |
0.33 |
0.28 |
0.69 |
1.09 |
| co05 |
495 |
0.79 |
0.78 |
0.78 |
0.70 |
0.44 |
1.11 |
| co06 |
495 |
0.68 |
0.67 |
0.62 |
0.56 |
0.50 |
1.11 |
| co08 |
495 |
0.44 |
0.44 |
0.34 |
0.29 |
0.38 |
1.04 |
| co09 |
495 |
0.76 |
0.76 |
0.75 |
0.67 |
0.41 |
1.07 |
| co10 |
495 |
0.68 |
0.68 |
0.62 |
0.57 |
0.49 |
1.10 |
co2$item.stats %>%
ggplot(aes(x = row.names(co2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(co2$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)
Table 13: Consistency. Subscale statistics when item drop
| co01 |
0.79 |
0.79 |
0.80 |
0.32 |
3.81 |
0.01 |
0.03 |
0.34 |
| co02 |
0.80 |
0.80 |
0.81 |
0.33 |
3.89 |
0.01 |
0.03 |
0.35 |
| co03 |
0.82 |
0.82 |
0.82 |
0.36 |
4.49 |
0.01 |
0.03 |
0.41 |
| co04 |
0.84 |
0.83 |
0.83 |
0.39 |
5.04 |
0.01 |
0.02 |
0.41 |
| co05 |
0.79 |
0.79 |
0.79 |
0.31 |
3.66 |
0.01 |
0.02 |
0.33 |
| co06 |
0.80 |
0.80 |
0.81 |
0.34 |
4.06 |
0.01 |
0.03 |
0.35 |
| co08 |
0.83 |
0.83 |
0.83 |
0.39 |
5.01 |
0.01 |
0.02 |
0.40 |
| co09 |
0.79 |
0.79 |
0.80 |
0.32 |
3.74 |
0.01 |
0.02 |
0.34 |
| co10 |
0.80 |
0.80 |
0.81 |
0.34 |
4.04 |
0.01 |
0.03 |
0.35 |
kable(co2$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)
Table 14: Consistency. Non missing response frequency for each item
| co01 |
0.05 |
0.11 |
0.31 |
0.39 |
0.11 |
0.03 |
0 |
| co02 |
0.03 |
0.12 |
0.32 |
0.38 |
0.13 |
0.02 |
0 |
| co03 |
0.02 |
0.07 |
0.22 |
0.44 |
0.21 |
0.04 |
0 |
| co04 |
0.01 |
0.04 |
0.10 |
0.35 |
0.32 |
0.18 |
0 |
| co05 |
0.06 |
0.19 |
0.37 |
0.27 |
0.09 |
0.02 |
0 |
| co06 |
0.04 |
0.14 |
0.28 |
0.38 |
0.13 |
0.03 |
0 |
| co08 |
0.08 |
0.27 |
0.42 |
0.16 |
0.06 |
0.01 |
0 |
| co09 |
0.06 |
0.24 |
0.40 |
0.22 |
0.06 |
0.02 |
0 |
| co10 |
0.04 |
0.14 |
0.33 |
0.34 |
0.11 |
0.03 |
0 |
Utility
ut2 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)
kable(ut2$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)
Table 15: Utility. Subscale statistics
|
0.88 |
0.88 |
0.88 |
0.4 |
7.4 |
0.01 |
35.92 |
8.32 |
0.41 |
ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)
Table 16: Utility. Items statistics
| ut01 |
495 |
0.79 |
0.79 |
0.79 |
0.73 |
0.76 |
1.05 |
| ut02 |
495 |
0.83 |
0.84 |
0.84 |
0.79 |
0.70 |
1.05 |
| ut03 |
495 |
0.55 |
0.56 |
0.49 |
0.45 |
0.71 |
1.11 |
| ut04 |
495 |
0.53 |
0.53 |
0.46 |
0.42 |
0.62 |
1.11 |
| ut05 |
495 |
0.69 |
0.68 |
0.64 |
0.60 |
0.61 |
1.21 |
| ut06 |
495 |
0.77 |
0.77 |
0.75 |
0.71 |
0.65 |
1.10 |
| ut07 |
495 |
0.62 |
0.62 |
0.57 |
0.52 |
0.64 |
1.13 |
| ut08 |
495 |
0.66 |
0.67 |
0.62 |
0.58 |
0.69 |
1.05 |
| ut09 |
495 |
0.70 |
0.70 |
0.66 |
0.62 |
0.64 |
1.17 |
| ut11 |
495 |
0.57 |
0.56 |
0.49 |
0.46 |
0.53 |
1.23 |
| ut12 |
495 |
0.72 |
0.72 |
0.68 |
0.65 |
0.63 |
1.15 |
ut2$item.stats %>%
ggplot(aes(x = row.names(ut2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(ut2$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)
Table 17: Utility. Subscale statistics when item drop
| ut01 |
0.86 |
0.86 |
0.86 |
0.38 |
6.21 |
0.01 |
0.01 |
0.40 |
| ut02 |
0.86 |
0.86 |
0.86 |
0.38 |
6.01 |
0.01 |
0.01 |
0.37 |
| ut03 |
0.88 |
0.88 |
0.88 |
0.42 |
7.30 |
0.01 |
0.01 |
0.43 |
| ut04 |
0.88 |
0.88 |
0.88 |
0.43 |
7.41 |
0.01 |
0.01 |
0.43 |
| ut05 |
0.87 |
0.87 |
0.87 |
0.40 |
6.69 |
0.01 |
0.01 |
0.41 |
| ut06 |
0.86 |
0.86 |
0.87 |
0.39 |
6.30 |
0.01 |
0.01 |
0.41 |
| ut07 |
0.87 |
0.87 |
0.87 |
0.41 |
6.99 |
0.01 |
0.01 |
0.42 |
| ut08 |
0.87 |
0.87 |
0.87 |
0.40 |
6.77 |
0.01 |
0.01 |
0.41 |
| ut09 |
0.87 |
0.87 |
0.87 |
0.40 |
6.64 |
0.01 |
0.02 |
0.41 |
| ut11 |
0.88 |
0.88 |
0.88 |
0.42 |
7.27 |
0.01 |
0.01 |
0.44 |
| ut12 |
0.86 |
0.87 |
0.87 |
0.39 |
6.52 |
0.01 |
0.02 |
0.41 |
kable(ut2$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)
Table 18: Utility. Non missing response frequency for each item
| ut01 |
0.01 |
0.01 |
0.06 |
0.29 |
0.34 |
0.28 |
0 |
| ut02 |
0.01 |
0.02 |
0.09 |
0.38 |
0.30 |
0.20 |
0 |
| ut03 |
0.01 |
0.04 |
0.09 |
0.33 |
0.31 |
0.22 |
0 |
| ut04 |
0.02 |
0.08 |
0.15 |
0.40 |
0.27 |
0.09 |
0 |
| ut05 |
0.03 |
0.06 |
0.20 |
0.35 |
0.23 |
0.12 |
0 |
| ut06 |
0.03 |
0.03 |
0.13 |
0.39 |
0.30 |
0.12 |
0 |
| ut07 |
0.01 |
0.05 |
0.19 |
0.35 |
0.26 |
0.14 |
0 |
| ut08 |
0.01 |
0.03 |
0.11 |
0.36 |
0.34 |
0.15 |
0 |
| ut09 |
0.03 |
0.05 |
0.15 |
0.38 |
0.25 |
0.13 |
0 |
| ut11 |
0.05 |
0.12 |
0.27 |
0.32 |
0.18 |
0.07 |
0 |
| ut12 |
0.02 |
0.07 |
0.15 |
0.38 |
0.26 |
0.12 |
0 |
Dependability
de2 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)
kable(de2$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)
Table 23: Dependability. Subscale statistics
|
0.82 |
0.82 |
0.84 |
0.31 |
4.49 |
0.01 |
26.19 |
7.05 |
0.32 |
de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)
Table 24: Dependability. Items statistics
| de01 |
495 |
0.58 |
0.59 |
0.53 |
0.47 |
0.52 |
1.10 |
| de02 |
495 |
0.72 |
0.72 |
0.68 |
0.62 |
0.43 |
1.15 |
| de03 |
495 |
0.65 |
0.65 |
0.60 |
0.54 |
0.43 |
1.19 |
| de05 |
495 |
0.62 |
0.62 |
0.59 |
0.50 |
0.71 |
1.16 |
| de06 |
495 |
0.67 |
0.67 |
0.62 |
0.56 |
0.45 |
1.23 |
| de07 |
495 |
0.61 |
0.62 |
0.56 |
0.51 |
0.56 |
1.00 |
| de08 |
495 |
0.70 |
0.71 |
0.67 |
0.61 |
0.53 |
1.06 |
| de09 |
495 |
0.46 |
0.45 |
0.40 |
0.31 |
0.69 |
1.20 |
| de10 |
495 |
0.73 |
0.73 |
0.71 |
0.64 |
0.45 |
1.18 |
| de11 |
495 |
0.41 |
0.40 |
0.28 |
0.25 |
0.46 |
1.20 |
de2$item.stats %>%
ggplot(aes(x = row.names(de2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(de2$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)
Table 25: Dependability. Subscale statistics when item drop
| de01 |
0.80 |
0.80 |
0.82 |
0.31 |
4.11 |
0.01 |
0.02 |
0.31 |
| de02 |
0.79 |
0.79 |
0.81 |
0.29 |
3.72 |
0.01 |
0.02 |
0.29 |
| de03 |
0.79 |
0.80 |
0.82 |
0.30 |
3.93 |
0.01 |
0.02 |
0.29 |
| de05 |
0.80 |
0.80 |
0.80 |
0.31 |
4.04 |
0.01 |
0.02 |
0.35 |
| de06 |
0.79 |
0.80 |
0.81 |
0.30 |
3.88 |
0.01 |
0.02 |
0.31 |
| de07 |
0.80 |
0.80 |
0.82 |
0.31 |
4.02 |
0.01 |
0.02 |
0.29 |
| de08 |
0.79 |
0.79 |
0.81 |
0.29 |
3.75 |
0.01 |
0.02 |
0.29 |
| de09 |
0.82 |
0.82 |
0.82 |
0.34 |
4.60 |
0.01 |
0.02 |
0.35 |
| de10 |
0.78 |
0.79 |
0.80 |
0.29 |
3.67 |
0.01 |
0.02 |
0.29 |
| de11 |
0.83 |
0.83 |
0.84 |
0.35 |
4.79 |
0.01 |
0.02 |
0.37 |
kable(de2$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)
Table 26: Dependability. Non missing response frequency for each item
| de01 |
0.05 |
0.11 |
0.24 |
0.43 |
0.14 |
0.03 |
0 |
| de02 |
0.09 |
0.17 |
0.36 |
0.26 |
0.10 |
0.02 |
0 |
| de03 |
0.10 |
0.17 |
0.35 |
0.27 |
0.09 |
0.03 |
0 |
| de05 |
0.02 |
0.03 |
0.10 |
0.28 |
0.34 |
0.23 |
0 |
| de06 |
0.10 |
0.17 |
0.32 |
0.28 |
0.11 |
0.03 |
0 |
| de07 |
0.02 |
0.06 |
0.27 |
0.42 |
0.20 |
0.03 |
0 |
| de08 |
0.04 |
0.10 |
0.24 |
0.44 |
0.15 |
0.03 |
0 |
| de09 |
0.01 |
0.06 |
0.13 |
0.27 |
0.33 |
0.20 |
0 |
| de10 |
0.10 |
0.15 |
0.30 |
0.34 |
0.10 |
0.02 |
0 |
| de11 |
0.07 |
0.20 |
0.28 |
0.31 |
0.12 |
0.03 |
0 |
Reliability measures
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)

Omega
Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
digits = digits, title = title, sl = sl, labels = labels,
plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
covar = covar)
Alpha: 0.94
G.6: 0.97
Omega Hierarchical: 0.6
Omega H asymptotic: 0.62
Omega Total 0.96
Schmid Leiman Factor loadings greater than 0.2
g F1* F2* F3* F4* F5* F6* h2 u2 p2
pr01 0.57 0.34 0.26 0.57 0.43 0.58
pr02 0.45 0.32 0.68 0.65
pr03 0.20 0.54 0.39 0.61 0.10
pr04 0.43 0.24 0.76 0.02
pr05 0.56 0.45 0.52 0.48 0.60
pr06 0.43 0.37 0.35 0.65 0.53
pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
pr08 0.52 0.38 0.23 0.49 0.51 0.54
pr09 0.41 0.29 0.29 0.71 0.57
pr10 0.37 0.25 0.23 0.77 0.58
co01 0.51 0.51 0.54 0.46 0.48
co02 0.44 0.55 0.49 0.51 0.39
co03 0.47 0.29 0.37 0.63 0.60
co04 0.33 0.44 0.38 0.62 0.28
co05 0.44 0.63 0.60 0.40 0.33
co06 0.37 0.51 0.40 0.60 0.34
co07- 0.29 0.17 0.83 0.11
co08 0.34 -0.37 0.41 0.59 0.02
co09 0.40 0.60 0.54 0.46 0.29
co10 0.38 0.45 0.40 0.60 0.36
ut01 0.36 0.67 0.61 0.39 0.21
ut02 0.45 0.63 0.64 0.36 0.31
ut03 0.22 0.32 0.52 0.54 0.46 0.09
ut04 0.27 0.39 0.24 0.76 0.31
ut05 0.41 0.44 0.37 0.63 0.45
ut06 0.46 0.60 0.56 0.44 0.38
ut07 0.34 0.46 0.34 0.66 0.35
ut08 0.40 0.47 0.40 0.60 0.39
ut09 0.42 0.46 0.39 0.61 0.44
ut10 0.24 0.09 0.91 0.03
ut11 0.54 0.21 0.39 0.48 0.52 0.61
ut12 0.46 0.47 0.45 0.55 0.47
fa01 0.44 0.61 0.60 0.40 0.33
fa02 0.67 0.54 0.46 0.00
fa03 -0.37 0.31 0.69 0.12
fa04 0.35 0.32 0.21 0.40 0.60 0.32
fa05 0.47 0.60 0.62 0.38 0.36
fa06 0.58 0.30 0.52 0.48 0.65
fa07 0.23 0.20 0.47 0.36 0.64 0.14
fa08 0.61 0.49 0.51 0.00
fa09 0.64 0.23 0.55 0.45 0.02
fa10 0.24 0.63 0.48 0.52 0.12
de01 0.43 0.29 0.71 0.63
de02 0.57 0.23 0.30 0.49 0.51 0.67
de03 0.50 0.28 0.38 0.62 0.65
de04- 0.31 0.39 0.27 0.73 0.37
de05 0.38 0.41 0.29 0.45 0.55 0.32
de06 0.58 0.47 0.58 0.42 0.59
de07 0.49 0.34 0.40 0.60 0.59
de08 0.54 0.23 0.25 0.43 0.57 0.68
de09 0.22 0.59 0.43 0.57 0.12
de10 0.60 0.40 0.56 0.44 0.64
de11 0.22 0.24 0.32 0.27 0.73 0.18
un01 0.25 0.72 0.63 0.37 0.10
un02 0.28 0.79 0.70 0.30 0.11
un03 0.43 -0.26 0.36 0.64 0.10
un04 0.25 0.68 0.53 0.47 0.12
un05 0.28 0.77 0.67 0.33 0.11
un06 -0.29 0.51 0.32 0.40 0.60 0.02
un07 0.33 0.59 0.54 0.46 0.20
un08 0.24 0.73 0.60 0.40 0.10
un09 0.31 0.61 0.52 0.48 0.18
un10 0.27 0.65 0.57 0.43 0.13
un11 0.24 0.72 0.60 0.40 0.10
un12 0.26 0.68 0.57 0.43 0.12
With eigenvalues of:
g F1* F2* F3* F4* F5* F6*
9.4 4.6 5.6 2.8 2.7 1.6 2.3
general/max 1.68 max/min = 3.6
mean percent general = 0.32 with sd = 0.22 and cv of 0.68
Explained Common Variance of the general factor = 0.32
The degrees of freedom are 1705 and the fit is 8.03
The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.04
RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
BIC = -6825.22
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 2015 and the fit is 22.88
The number of observations was 495 with Chi Square = 10774.78 with prob < 0
The root mean square of the residuals is 0.15
The df corrected root mean square of the residuals is 0.15
RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
BIC = -1727.41
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.81 0.89 0.97 0.86
Multiple R square of scores with factors 0.66 0.79 0.94 0.74
Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47
F4* F5* F6*
Correlation of scores with factors 0.94 0.71 0.91
Multiple R square of scores with factors 0.88 0.50 0.83
Minimum correlation of factor score estimates 0.76 0.00 0.66
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.96 0.87 0.91 0.86
Omega general for total scores and subscales 0.60 0.43 0.11 0.45
Omega group for total scores and subscales 0.21 0.44 0.79 0.41
F4* F5* F6*
Omega total for total scores and subscales 0.83 0.81 0.51
Omega general for total scores and subscales 0.09 0.57 0.14
Omega group for total scores and subscales 0.74 0.24 0.37
splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")
Split half reliabilities
Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
key = NULL, use = "pairwise")
Maximum split half reliability (lambda 4) = 0.96
Guttman lambda 6 = 0.96
Average split half reliability = 0.93
Guttman lambda 3 (alpha) = 0.93
Guttman lambda 2 = 0.94
Minimum split half reliability (beta) = 0.87
Average interitem r = 0.17 with median = 0.18
guttman(taia %>% ungroup() %>% select(all_of(taia_items)))
Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
Alternative estimates of reliability
Guttman bounds
L1 = 0.92
L2 = 0.94
L3 (alpha) = 0.93
L4 (max) = 0.97
L5 = 0.92
L6 (smc) = 0.96
TenBerge bounds
mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
alpha of first PC = 0.95
estimated greatest lower bound based upon communalities= 0.97
beta found by splitHalf = 0.85
glb.fa(taia %>% ungroup() %>% select(all_of(taia_items)))
$glb
[1] 0.965817
$communality
pr01 pr02 pr03 pr04 pr05 pr06 pr07
0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103
pr08 pr09 pr10 co01 co02 co03 co04
0.6112839 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165
co05 co06 co07 co08 co09 co10 ut01
0.7521395 0.5726406 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201
ut02 ut03 ut04 ut05 ut06 ut07 ut08
0.7277348 0.6422830 0.3722353 0.5234489 0.7171434 0.5757501 0.5792722
ut09 ut10 ut11 ut12 fa01 fa02 fa03
0.7253062 0.3644927 0.6451266 0.5389414 0.6943597 0.5928412 0.4376811
fa04 fa05 fa06 fa07 fa08 fa09 fa10
0.5197275 0.7565165 0.5844505 0.4786126 0.6061162 0.6945327 0.4919291
de01 de02 de03 de04 de05 de06 de07
0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322 0.5278337
de08 de09 de10 de11 un01 un02 un03
0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
un04 un05 un06 un07 un08 un09 un10
0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091
un11 un12
0.6870705 0.6276157
$numf
[1] 19
$Call
glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))
Exploratory Factor Analysis
taia_items <- c(
pr_items, co_items, ut_items, fa_items, de_items, un_items
)
6 factors, varimax rotation
efa_6f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "varimax")
loadings(efa_6f_vm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
pr01 0.489 0.475 0.172 0.287
pr02 0.263 0.392 0.219 0.164 0.181
pr03 0.219 0.547
pr04 -0.109 0.139 0.437
pr05 0.204 0.350 0.139 0.123 0.676
pr06 0.475 0.279 0.144 0.105
pr07 0.387 0.525 0.208 0.134
pr08 0.492 0.429 0.125 0.241
pr09 0.350 0.347 0.133 0.220
pr10 0.328 0.301 0.124 0.108
co01 0.243 0.676 0.116
co02 0.169 0.626 0.115
co03 0.416 0.370 0.159 0.141
co04 0.532 0.147 0.121 0.161
co05 0.137 0.724
co06 0.152 0.554 -0.117
co08 -0.118 0.413 -0.115 -0.127 -0.467
co09 0.676 -0.160
co10 0.184 0.526 0.206 -0.123
ut01 0.810
ut02 0.806 0.102 0.135 0.125
ut03 0.472 -0.102 0.119 0.507
ut04 0.461 0.104 0.132
ut05 0.598 0.194
ut06 0.717 0.164 0.125
ut07 0.550 0.207
ut08 0.576 0.249
ut09 0.593 0.174 0.102 0.128
ut11 0.368 0.255 0.158 0.126 0.591
ut12 0.604 0.233 0.135 0.142
fa01 0.300 0.345 0.628
fa02 -0.207 0.669 0.148
fa03 -0.116 0.385 -0.318 0.148
fa04 0.549 0.103 -0.192 0.177
fa05 0.293 0.411 0.622
fa06 0.301 0.573 0.126 0.188 0.182 0.163
fa07 0.196 0.474 0.193
fa08 -0.191 0.602 0.232
fa09 -0.155 0.631 0.310
fa10 0.280 0.136 0.599
de01 0.278 0.429 0.162
de02 0.225 0.571 0.129 0.155 0.228
de03 0.223 0.475 0.115 0.138 0.214
de05 0.508 0.173 0.137 0.385
de06 0.218 0.352 0.184 0.171 0.666
de07 0.439 0.301 0.220 0.182
de08 0.345 0.412 0.193 0.107 0.185 0.205
de09 0.259 0.592
de10 0.287 0.512 0.177 0.351
de11 0.181 0.332 0.255
un01 0.287 0.731
un02 0.138 0.815
un03 0.207 0.502 -0.247
un04 0.113 0.711
un05 0.191 0.791
un06 -0.131 0.518 0.189
un07 0.286 0.648 -0.139 0.128
un08 0.182 0.746
un09 0.162 0.662 0.197
un10 0.261 0.690
un11 0.763
un12 0.236 0.713
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
SS loadings 7.605 7.077 6.534 2.832 2.786 2.029
Proportion Var 0.123 0.114 0.105 0.046 0.045 0.033
Cumulative Var 0.123 0.237 0.342 0.388 0.433 0.466
kable(sort(efa_6f_vm$uniquenesses, decreasing = TRUE), col.names = "U")
| de11 |
0.7817421 |
| pr04 |
0.7700534 |
| pr10 |
0.7616189 |
| ut04 |
0.7559106 |
| fa03 |
0.7134353 |
| de01 |
0.7002496 |
| fa07 |
0.6907293 |
| pr09 |
0.6888357 |
| un06 |
0.6751271 |
| pr02 |
0.6694849 |
| pr06 |
0.6576002 |
| ut07 |
0.6510578 |
| co04 |
0.6481729 |
| co06 |
0.6474121 |
| de03 |
0.6464066 |
| un03 |
0.6402866 |
| pr03 |
0.6384384 |
| co03 |
0.6327467 |
| co10 |
0.6282188 |
| de07 |
0.6246089 |
| fa04 |
0.6137315 |
| ut08 |
0.5910384 |
| ut05 |
0.5868681 |
| ut09 |
0.5865585 |
| de08 |
0.5858475 |
| de09 |
0.5766570 |
| co08 |
0.5671255 |
| co02 |
0.5579891 |
| de05 |
0.5437340 |
| fa10 |
0.5420869 |
| ut12 |
0.5382611 |
| fa08 |
0.5284220 |
| de02 |
0.5246474 |
| co09 |
0.5063667 |
| pr07 |
0.4996945 |
| pr08 |
0.4983024 |
| ut03 |
0.4937529 |
| de10 |
0.4934491 |
| un09 |
0.4875879 |
| fa09 |
0.4737183 |
| fa02 |
0.4731288 |
| fa06 |
0.4702934 |
| un04 |
0.4642368 |
| co01 |
0.4635405 |
| un07 |
0.4574466 |
| co05 |
0.4479825 |
| ut06 |
0.4362455 |
| un10 |
0.4358851 |
| un12 |
0.4312571 |
| pr01 |
0.4144342 |
| ut11 |
0.4071750 |
| un08 |
0.4066783 |
| un11 |
0.3931924 |
| fa01 |
0.3816574 |
| un01 |
0.3730108 |
| fa05 |
0.3486393 |
| pr05 |
0.3357658 |
| ut01 |
0.3321338 |
| un05 |
0.3254634 |
| de06 |
0.3122163 |
| un02 |
0.3085120 |
| ut02 |
0.3006345 |
6 factors, promax rotation
efa_6f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "promax")
loadings(efa_6f_pm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
pr01 0.461 0.225 0.283
pr02 0.338 0.110 0.148 0.136
pr03 -0.110 0.614
pr04 -0.141 0.484
pr05 0.790
pr06 0.174 0.410
pr07 0.527 0.131 0.192
pr08 0.422 0.278 0.238
pr09 0.361 0.146 0.227
pr10 0.263 0.204 -0.122
co01 0.809 0.102 -0.145
co02 0.765 -0.137
co03 0.303 0.264 0.114
co04 0.493 0.121 -0.132
co05 0.902 -0.102 -0.117 -0.109
co06 0.668 -0.154
co08 0.542 -0.180 -0.501 -0.100
co09 0.817 -0.113 -0.123 -0.169
co10 0.598 0.115 -0.135
ut01 -0.249 0.974
ut02 -0.231 0.910 0.113
ut03 -0.213 0.385 0.515
ut04 -0.198 0.552 0.146
ut05 0.658 -0.124
ut06 0.789 0.108
ut07 0.128 0.593
ut08 0.186 0.543 -0.108
ut09 0.608 0.110
ut11 0.291 0.694
ut12 0.106 0.559
fa01 0.277 0.125 0.171 0.650
fa02 -0.129 0.695
fa03 0.418 -0.154 -0.379 0.158
fa04 0.609 -0.225 -0.241 0.146
fa05 0.367 0.105 0.156 -0.119 0.648
fa06 0.580 0.149 0.131
fa07 -0.218 0.508 0.109 0.173
fa08 -0.179 0.165 0.604
fa09 -0.187 0.254 0.627
fa10 0.281 -0.127 0.648 -0.119
de01 0.460
de02 0.569 0.135 0.166
de03 0.430 0.173
de05 0.114 0.340 0.407 -0.107
de06 0.770
de07 0.160 0.109 0.337 0.156
de08 0.329 0.129 0.141 0.153
de09 0.692 -0.101
de10 0.397 0.106 0.103 0.339
de11 -0.106 -0.103 0.320 0.101 0.273
un01 -0.148 0.804 0.225 0.123 -0.141
un02 0.857 -0.105
un03 0.451 0.145 -0.247
un04 0.734
un05 0.844
un06 0.556 -0.347 0.256
un07 0.192 0.641 -0.115 -0.172 0.113
un08 -0.114 0.817 0.113
un09 0.646 -0.101 0.212
un10 0.219 0.698 -0.212
un11 0.803 -0.103
un12 -0.180 0.740 0.166
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
SS loadings 7.535 6.547 6.389 3.041 2.823 2.419
Proportion Var 0.122 0.106 0.103 0.049 0.046 0.039
Cumulative Var 0.122 0.227 0.330 0.379 0.425 0.464
kable(
sort(efa_6f_pm$uniquenesses, decreasing = TRUE),
col.names = "U")
| de11 |
0.7817421 |
| pr04 |
0.7700534 |
| pr10 |
0.7616189 |
| ut04 |
0.7559106 |
| fa03 |
0.7134353 |
| de01 |
0.7002496 |
| fa07 |
0.6907293 |
| pr09 |
0.6888357 |
| un06 |
0.6751271 |
| pr02 |
0.6694849 |
| pr06 |
0.6576002 |
| ut07 |
0.6510578 |
| co04 |
0.6481729 |
| co06 |
0.6474121 |
| de03 |
0.6464066 |
| un03 |
0.6402866 |
| pr03 |
0.6384384 |
| co03 |
0.6327467 |
| co10 |
0.6282188 |
| de07 |
0.6246089 |
| fa04 |
0.6137315 |
| ut08 |
0.5910384 |
| ut05 |
0.5868681 |
| ut09 |
0.5865585 |
| de08 |
0.5858475 |
| de09 |
0.5766570 |
| co08 |
0.5671255 |
| co02 |
0.5579891 |
| de05 |
0.5437340 |
| fa10 |
0.5420869 |
| ut12 |
0.5382611 |
| fa08 |
0.5284220 |
| de02 |
0.5246474 |
| co09 |
0.5063667 |
| pr07 |
0.4996945 |
| pr08 |
0.4983024 |
| ut03 |
0.4937529 |
| de10 |
0.4934491 |
| un09 |
0.4875879 |
| fa09 |
0.4737183 |
| fa02 |
0.4731288 |
| fa06 |
0.4702934 |
| un04 |
0.4642368 |
| co01 |
0.4635405 |
| un07 |
0.4574466 |
| co05 |
0.4479825 |
| ut06 |
0.4362455 |
| un10 |
0.4358851 |
| un12 |
0.4312571 |
| pr01 |
0.4144342 |
| ut11 |
0.4071750 |
| un08 |
0.4066783 |
| un11 |
0.3931924 |
| fa01 |
0.3816574 |
| un01 |
0.3730108 |
| fa05 |
0.3486393 |
| pr05 |
0.3357658 |
| ut01 |
0.3321338 |
| un05 |
0.3254634 |
| de06 |
0.3122163 |
| un02 |
0.3085120 |
| ut02 |
0.3006345 |
5 factors, varimax rotation
efa_5f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "varimax")
loadings(efa_5f_vm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5
pr01 0.576 0.372 0.170 0.212
pr02 0.319 0.359 0.221 0.205
pr03 0.306 -0.108 0.412 0.121
pr04 0.131 -0.197 0.340 0.154
pr05 0.211 0.453 0.156 0.482
pr06 0.501 0.249 0.142 0.100
pr07 0.456 0.465 0.232
pr08 0.567 0.334 0.123 0.160
pr09 0.424 0.257 0.129 0.129
pr10 0.369 0.262 0.124 0.104
co01 0.303 0.625 0.130
co02 0.235 0.573 0.114 0.112
co03 0.462 0.329 0.176
co04 0.573 0.115
co05 0.197 0.690
co06 0.207 0.520
co08 -0.157 0.505 -0.112 -0.326 -0.146
co09 0.121 0.681 -0.100
co10 0.219 0.513 0.206 -0.125
ut01 0.788
ut02 0.792 0.100 0.136
ut03 0.531 -0.225 0.358 0.140
ut04 0.437 0.115
ut05 0.586 0.191
ut06 0.713 0.148
ut07 0.559 0.171
ut08 0.612 0.179
ut09 0.601 0.152 0.128
ut11 0.362 0.341 0.171 0.431
ut12 0.632 0.179 0.131 0.115
fa01 0.317 0.338 0.123 0.618
fa02 -0.206 0.202 0.659
fa03 -0.148 0.484 -0.103
fa04 0.616
fa05 0.305 0.412 0.604
fa06 0.366 0.528 0.127 0.241 0.179
fa07 0.512 0.182
fa08 -0.190 0.284 0.591
fa09 -0.155 0.321 0.632
fa10 0.278 0.126 0.611
de01 0.320 0.386 0.159
de02 0.282 0.553 0.132 0.281
de03 0.246 0.493 0.117 0.176 0.105
de05 0.583 0.133 0.230
de06 0.219 0.462 0.197 0.470
de07 0.462 0.290 0.220 0.182
de08 0.392 0.386 0.195 0.273
de09 0.367 0.374
de10 0.300 0.558 0.303 0.121
de11 0.445 0.153
un01 0.301 0.727
un02 0.124 0.815
un03 0.221 0.501 -0.243
un04 0.107 0.116 0.712
un05 0.219 0.787
un06 0.519 0.148
un07 0.328 0.651
un08 0.195 0.744
un09 0.198 0.667 0.124 -0.110
un10 0.272 0.692 -0.101
un11 0.107 0.764
un12 0.242 0.712
Factor1 Factor2 Factor3 Factor4 Factor5
SS loadings 8.695 6.882 6.542 2.836 2.744
Proportion Var 0.140 0.111 0.106 0.046 0.044
Cumulative Var 0.140 0.251 0.357 0.403 0.447
kable(
sort(efa_5f_vm$uniquenesses, decreasing = TRUE),
col.names = "U"
)
| pr04 |
0.8036037 |
| ut04 |
0.7875168 |
| de11 |
0.7660851 |
| pr10 |
0.7648954 |
| fa03 |
0.7322846 |
| pr09 |
0.7157810 |
| de09 |
0.7138430 |
| de01 |
0.7115636 |
| pr03 |
0.7071547 |
| un06 |
0.6984492 |
| fa07 |
0.6934404 |
| pr02 |
0.6780781 |
| co06 |
0.6691958 |
| pr06 |
0.6557484 |
| ut07 |
0.6500545 |
| co04 |
0.6435044 |
| de03 |
0.6411034 |
| un03 |
0.6404520 |
| co03 |
0.6336224 |
| co10 |
0.6291329 |
| de07 |
0.6207641 |
| ut05 |
0.6108805 |
| fa04 |
0.6096628 |
| ut09 |
0.5950296 |
| co02 |
0.5895055 |
| ut08 |
0.5880908 |
| de05 |
0.5859234 |
| co08 |
0.5800904 |
| de08 |
0.5776240 |
| ut12 |
0.5381209 |
| ut11 |
0.5378917 |
| fa10 |
0.5312032 |
| fa08 |
0.5256412 |
| pr08 |
0.5255447 |
| ut03 |
0.5181711 |
| de02 |
0.5158859 |
| co09 |
0.5114014 |
| pr07 |
0.5096638 |
| co01 |
0.4937745 |
| pr05 |
0.4926627 |
| de10 |
0.4869821 |
| un09 |
0.4861097 |
| fa06 |
0.4806029 |
| co05 |
0.4747549 |
| fa09 |
0.4734086 |
| fa02 |
0.4713133 |
| de06 |
0.4691654 |
| un04 |
0.4646410 |
| un07 |
0.4595727 |
| ut06 |
0.4594009 |
| pr01 |
0.4496805 |
| un10 |
0.4361787 |
| un12 |
0.4325425 |
| un08 |
0.4008023 |
| un11 |
0.3971241 |
| fa01 |
0.3854741 |
| ut01 |
0.3691461 |
| un01 |
0.3671937 |
| fa05 |
0.3613714 |
| ut02 |
0.3407660 |
| un05 |
0.3303704 |
| un02 |
0.3106689 |
5 factors, promax rotation
efa_5f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "promax")
loadings(efa_5f_pm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5
pr01 0.446 0.279 0.163
pr02 0.143 0.296 0.114 0.211
pr03 0.233 -0.214 -0.117 0.424
pr04 -0.293 0.354 0.107
pr05 -0.115 0.392 0.579
pr06 0.456 0.170
pr07 0.281 0.412 0.218
pr08 0.480 0.250 0.106
pr09 0.342 0.189
pr10 0.300 0.206 -0.112
co01 0.146 0.650 0.122
co02 0.599 0.111
co03 0.354 0.265 0.139
co04 0.624
co05 0.759 -0.112
co06 0.545 -0.122
co08 -0.181 0.655 -0.178 -0.313 -0.109
co09 0.763 -0.105 -0.100
co10 0.114 0.531 0.122 -0.157
ut01 0.932 -0.173 -0.161
ut02 0.859 -0.129
ut03 0.556 -0.375 0.303
ut04 0.452
ut05 0.610 0.111
ut06 0.757
ut07 0.631 0.114 -0.191
ut08 0.655 -0.102
ut09 0.605
ut11 0.121 0.247 0.486
ut12 0.625
fa01 0.152 0.310 0.125 0.634
fa02 -0.203 0.143 0.681
fa03 -0.291 0.581
fa04 -0.281 0.695
fa05 0.129 0.400 0.620
fa06 0.134 0.490 0.229 0.130
fa07 -0.104 0.583 0.102
fa08 -0.210 0.254 0.592
fa09 -0.158 0.280 0.627
fa10 0.247 0.110 -0.175 0.648
de01 0.200 0.356
de02 0.524 0.319
de03 0.478 0.183
de05 0.562 0.179
de06 -0.118 0.396 0.550
de07 0.346 0.199 0.104 0.156
de08 0.192 0.310 0.272
de09 0.305 -0.213 0.381
de10 0.534 0.332
de11 -0.110 0.509
un01 0.258 -0.197 0.810 -0.169 0.132
un02 0.857
un03 0.185 0.450 -0.246
un04 0.739
un05 0.103 -0.121 0.847
un06 -0.250 -0.119 0.556 0.200
un07 -0.175 0.266 0.641
un08 0.101 -0.128 0.827 -0.111 0.126
un09 -0.166 0.647 0.170
un10 -0.202 0.204 0.699
un11 0.805
un12 0.151 -0.155 0.746
Factor1 Factor2 Factor3 Factor4 Factor5
SS loadings 7.635 6.936 6.600 3.174 2.713
Proportion Var 0.123 0.112 0.106 0.051 0.044
Cumulative Var 0.123 0.235 0.341 0.393 0.436
kable(
sort(efa_5f_pm$uniquenesses, decreasing = TRUE),
col.names = "U"
)
| pr04 |
0.8036037 |
| ut04 |
0.7875168 |
| de11 |
0.7660851 |
| pr10 |
0.7648954 |
| fa03 |
0.7322846 |
| pr09 |
0.7157810 |
| de09 |
0.7138430 |
| de01 |
0.7115636 |
| pr03 |
0.7071547 |
| un06 |
0.6984492 |
| fa07 |
0.6934404 |
| pr02 |
0.6780781 |
| co06 |
0.6691958 |
| pr06 |
0.6557484 |
| ut07 |
0.6500545 |
| co04 |
0.6435044 |
| de03 |
0.6411034 |
| un03 |
0.6404520 |
| co03 |
0.6336224 |
| co10 |
0.6291329 |
| de07 |
0.6207641 |
| ut05 |
0.6108805 |
| fa04 |
0.6096628 |
| ut09 |
0.5950296 |
| co02 |
0.5895055 |
| ut08 |
0.5880908 |
| de05 |
0.5859234 |
| co08 |
0.5800904 |
| de08 |
0.5776240 |
| ut12 |
0.5381209 |
| ut11 |
0.5378917 |
| fa10 |
0.5312032 |
| fa08 |
0.5256412 |
| pr08 |
0.5255447 |
| ut03 |
0.5181711 |
| de02 |
0.5158859 |
| co09 |
0.5114014 |
| pr07 |
0.5096638 |
| co01 |
0.4937745 |
| pr05 |
0.4926627 |
| de10 |
0.4869821 |
| un09 |
0.4861097 |
| fa06 |
0.4806029 |
| co05 |
0.4747549 |
| fa09 |
0.4734086 |
| fa02 |
0.4713133 |
| de06 |
0.4691654 |
| un04 |
0.4646410 |
| un07 |
0.4595727 |
| ut06 |
0.4594009 |
| pr01 |
0.4496805 |
| un10 |
0.4361787 |
| un12 |
0.4325425 |
| un08 |
0.4008023 |
| un11 |
0.3971241 |
| fa01 |
0.3854741 |
| ut01 |
0.3691461 |
| un01 |
0.3671937 |
| fa05 |
0.3613714 |
| ut02 |
0.3407660 |
| un05 |
0.3303704 |
| un02 |
0.3106689 |
Confirmatory Factor Analysis
Basic model
Model:
mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"
CFA model fitting:
model1 <- cfa(mdl1, taia %>% select(all_of(taia_items)))
summary(model1)
lavaan 0.6-8 ended normally after 62 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 139
Number of observations 495
Model Test User Model:
Test statistic 6326.223
Degrees of freedom 1814
P-value (Chi-square) 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
PR =~
pr01 1.000
pr02 0.727 0.055 13.199 0.000
pr03 0.386 0.060 6.384 0.000
pr04 0.153 0.063 2.431 0.015
pr05 0.870 0.068 12.721 0.000
pr06 0.808 0.061 13.285 0.000
pr07 1.040 0.061 17.095 0.000
pr08 0.847 0.050 17.029 0.000
pr09 0.714 0.054 13.167 0.000
pr10 0.666 0.060 11.105 0.000
CO =~
co01 1.000
co02 0.896 0.061 14.596 0.000
co03 0.646 0.061 10.618 0.000
co04 0.459 0.065 7.065 0.000
co05 1.090 0.065 16.652 0.000
co06 0.856 0.066 13.052 0.000
co08 0.438 0.062 7.047 0.000
co09 0.978 0.063 15.527 0.000
co10 0.831 0.065 12.804 0.000
UT =~
ut01 1.000
ut02 1.083 0.055 19.815 0.000
ut03 0.682 0.062 11.048 0.000
ut04 0.636 0.062 10.244 0.000
ut05 0.965 0.066 14.724 0.000
ut06 1.008 0.058 17.348 0.000
ut07 0.790 0.062 12.676 0.000
ut08 0.807 0.057 14.082 0.000
ut09 0.945 0.063 14.930 0.000
ut11 0.789 0.068 11.527 0.000
ut12 0.974 0.062 15.791 0.000
FA =~
fa01 1.000
fa02 0.535 0.060 8.869 0.000
fa03 0.219 0.060 3.670 0.000
fa04 0.424 0.056 7.575 0.000
fa05 1.028 0.051 20.280 0.000
fa06 0.716 0.053 13.558 0.000
fa07 0.335 0.057 5.911 0.000
fa08 0.487 0.058 8.366 0.000
fa09 0.626 0.059 10.519 0.000
fa10 0.785 0.059 13.357 0.000
DE =~
de01 1.000
de02 1.315 0.113 11.688 0.000
de03 1.183 0.111 10.668 0.000
de05 0.981 0.103 9.509 0.000
de06 1.276 0.116 11.019 0.000
de07 0.979 0.093 10.586 0.000
de08 1.185 0.102 11.579 0.000
de09 0.604 0.098 6.189 0.000
de10 1.377 0.116 11.853 0.000
de11 0.476 0.096 4.958 0.000
UN =~
un01 1.000
un02 1.215 0.064 18.860 0.000
un03 0.817 0.068 11.959 0.000
un04 1.025 0.062 16.485 0.000
un05 1.142 0.063 18.233 0.000
un06 0.783 0.072 10.830 0.000
un07 1.040 0.068 15.336 0.000
un08 1.120 0.066 16.897 0.000
un09 1.090 0.071 15.404 0.000
un10 1.050 0.066 15.883 0.000
un11 1.192 0.069 17.382 0.000
un12 1.065 0.064 16.524 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
PR ~~
CO 0.417 0.043 9.702 0.000
UT 0.479 0.045 10.634 0.000
FA 0.402 0.044 9.066 0.000
DE 0.423 0.045 9.495 0.000
UN 0.232 0.034 6.779 0.000
CO ~~
UT 0.280 0.038 7.332 0.000
FA 0.355 0.044 8.018 0.000
DE 0.326 0.039 8.350 0.000
UN 0.171 0.033 5.125 0.000
UT ~~
FA 0.331 0.043 7.706 0.000
DE 0.341 0.040 8.621 0.000
UN 0.187 0.034 5.560 0.000
FA ~~
DE 0.366 0.043 8.534 0.000
UN 0.061 0.035 1.738 0.082
DE ~~
UN 0.186 0.029 6.327 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.pr01 0.366 0.028 13.060 0.000
.pr02 0.619 0.041 14.916 0.000
.pr03 0.936 0.060 15.582 0.000
.pr04 1.071 0.068 15.711 0.000
.pr05 0.977 0.065 14.993 0.000
.pr06 0.751 0.050 14.902 0.000
.pr07 0.581 0.042 13.928 0.000
.pr08 0.390 0.028 13.953 0.000
.pr09 0.601 0.040 14.921 0.000
.pr10 0.805 0.053 15.208 0.000
.co01 0.526 0.040 13.135 0.000
.co02 0.573 0.041 13.824 0.000
.co03 0.780 0.052 15.010 0.000
.co04 1.045 0.068 15.460 0.000
.co05 0.481 0.039 12.355 0.000
.co06 0.763 0.053 14.429 0.000
.co08 0.957 0.062 15.462 0.000
.co09 0.536 0.040 13.296 0.000
.co10 0.763 0.053 14.505 0.000
.ut01 0.443 0.033 13.514 0.000
.ut02 0.333 0.027 12.266 0.000
.ut03 0.918 0.060 15.239 0.000
.ut04 0.959 0.063 15.322 0.000
.ut05 0.844 0.058 14.654 0.000
.ut06 0.528 0.038 13.844 0.000
.ut07 0.866 0.058 15.029 0.000
.ut08 0.674 0.046 14.788 0.000
.ut09 0.776 0.053 14.606 0.000
.ut11 1.107 0.073 15.183 0.000
.ut12 0.689 0.048 14.384 0.000
.fa01 0.393 0.036 11.016 0.000
.fa02 1.156 0.076 15.296 0.000
.fa03 1.245 0.079 15.664 0.000
.fa04 1.028 0.067 15.424 0.000
.fa05 0.348 0.034 10.153 0.000
.fa06 0.744 0.051 14.506 0.000
.fa07 1.092 0.070 15.551 0.000
.fa08 1.094 0.071 15.349 0.000
.fa09 1.071 0.071 15.086 0.000
.fa10 0.932 0.064 14.555 0.000
.de01 0.822 0.055 14.942 0.000
.de02 0.678 0.048 14.054 0.000
.de03 0.895 0.061 14.713 0.000
.de05 0.985 0.065 15.098 0.000
.de06 0.891 0.061 14.538 0.000
.de07 0.636 0.043 14.749 0.000
.de08 0.583 0.041 14.151 0.000
.de09 1.297 0.083 15.551 0.000
.de10 0.678 0.049 13.889 0.000
.de11 1.361 0.087 15.625 0.000
.un01 0.500 0.035 14.388 0.000
.un02 0.399 0.030 13.239 0.000
.un03 0.962 0.063 15.267 0.000
.un04 0.541 0.037 14.425 0.000
.un05 0.425 0.031 13.665 0.000
.un06 1.147 0.075 15.374 0.000
.un07 0.729 0.049 14.737 0.000
.un08 0.583 0.041 14.285 0.000
.un09 0.789 0.054 14.721 0.000
.un10 0.655 0.045 14.601 0.000
.un11 0.583 0.041 14.094 0.000
.un12 0.578 0.040 14.413 0.000
PR 0.605 0.059 10.207 0.000
CO 0.633 0.069 9.144 0.000
UT 0.659 0.066 9.942 0.000
FA 0.816 0.077 10.599 0.000
DE 0.377 0.058 6.490 0.000
UN 0.604 0.064 9.418 0.000
Fit measures:
kable(tibble(
`Model 1` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model1,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))
| Chi-Squared |
6326.2235 |
| DF |
1814.0000 |
| p |
0.0000 |
| GFI |
0.6381 |
| AGFI |
0.6104 |
| CFI |
0.7096 |
| TLI |
0.6973 |
| SRMR |
0.1010 |
| RMSEA |
0.0709 |
Standardized solution:
smodel1 <- standardizedsolution(model1)
Loadings:
kable(
smodel1 %>%
filter(op == "=~"),
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)
| PR |
=~ |
pr01 |
0.789 |
0.020 |
39.913 |
0.000 |
0.751 |
0.828 |
| PR |
=~ |
pr02 |
0.584 |
0.032 |
18.261 |
0.000 |
0.521 |
0.647 |
| PR |
=~ |
pr03 |
0.296 |
0.043 |
6.854 |
0.000 |
0.211 |
0.381 |
| PR |
=~ |
pr04 |
0.114 |
0.047 |
2.456 |
0.014 |
0.023 |
0.206 |
| PR |
=~ |
pr05 |
0.565 |
0.033 |
17.164 |
0.000 |
0.501 |
0.630 |
| PR |
=~ |
pr06 |
0.587 |
0.032 |
18.467 |
0.000 |
0.525 |
0.650 |
| PR |
=~ |
pr07 |
0.728 |
0.024 |
30.723 |
0.000 |
0.682 |
0.775 |
| PR |
=~ |
pr08 |
0.726 |
0.024 |
30.439 |
0.000 |
0.679 |
0.773 |
| PR |
=~ |
pr09 |
0.583 |
0.032 |
18.186 |
0.000 |
0.520 |
0.645 |
| PR |
=~ |
pr10 |
0.500 |
0.036 |
13.888 |
0.000 |
0.429 |
0.570 |
| CO |
=~ |
co01 |
0.739 |
0.024 |
30.666 |
0.000 |
0.692 |
0.786 |
| CO |
=~ |
co02 |
0.686 |
0.027 |
25.127 |
0.000 |
0.632 |
0.739 |
| CO |
=~ |
co03 |
0.503 |
0.037 |
13.698 |
0.000 |
0.431 |
0.575 |
| CO |
=~ |
co04 |
0.337 |
0.043 |
7.853 |
0.000 |
0.253 |
0.421 |
| CO |
=~ |
co05 |
0.781 |
0.022 |
36.274 |
0.000 |
0.739 |
0.823 |
| CO |
=~ |
co06 |
0.615 |
0.031 |
19.688 |
0.000 |
0.554 |
0.676 |
| CO |
=~ |
co08 |
0.336 |
0.043 |
7.829 |
0.000 |
0.252 |
0.420 |
| CO |
=~ |
co09 |
0.728 |
0.025 |
29.429 |
0.000 |
0.680 |
0.777 |
| CO |
=~ |
co10 |
0.604 |
0.032 |
18.958 |
0.000 |
0.541 |
0.666 |
| UT |
=~ |
ut01 |
0.773 |
0.021 |
37.597 |
0.000 |
0.733 |
0.814 |
| UT |
=~ |
ut02 |
0.836 |
0.016 |
51.142 |
0.000 |
0.804 |
0.868 |
| UT |
=~ |
ut03 |
0.500 |
0.036 |
13.954 |
0.000 |
0.430 |
0.570 |
| UT |
=~ |
ut04 |
0.466 |
0.037 |
12.493 |
0.000 |
0.393 |
0.539 |
| UT |
=~ |
ut05 |
0.649 |
0.028 |
22.945 |
0.000 |
0.593 |
0.704 |
| UT |
=~ |
ut06 |
0.748 |
0.022 |
33.612 |
0.000 |
0.704 |
0.791 |
| UT |
=~ |
ut07 |
0.568 |
0.033 |
17.378 |
0.000 |
0.504 |
0.632 |
| UT |
=~ |
ut08 |
0.624 |
0.030 |
21.010 |
0.000 |
0.566 |
0.682 |
| UT |
=~ |
ut09 |
0.657 |
0.028 |
23.611 |
0.000 |
0.602 |
0.711 |
| UT |
=~ |
ut11 |
0.520 |
0.035 |
14.890 |
0.000 |
0.452 |
0.589 |
| UT |
=~ |
ut12 |
0.690 |
0.026 |
26.662 |
0.000 |
0.639 |
0.741 |
| FA |
=~ |
fa01 |
0.822 |
0.019 |
42.299 |
0.000 |
0.784 |
0.860 |
| FA |
=~ |
fa02 |
0.410 |
0.040 |
10.123 |
0.000 |
0.330 |
0.489 |
| FA |
=~ |
fa03 |
0.175 |
0.047 |
3.752 |
0.000 |
0.083 |
0.266 |
| FA |
=~ |
fa04 |
0.353 |
0.042 |
8.337 |
0.000 |
0.270 |
0.436 |
| FA |
=~ |
fa05 |
0.844 |
0.018 |
46.444 |
0.000 |
0.809 |
0.880 |
| FA |
=~ |
fa06 |
0.600 |
0.032 |
18.680 |
0.000 |
0.537 |
0.663 |
| FA |
=~ |
fa07 |
0.279 |
0.044 |
6.264 |
0.000 |
0.191 |
0.366 |
| FA |
=~ |
fa08 |
0.388 |
0.041 |
9.407 |
0.000 |
0.307 |
0.469 |
| FA |
=~ |
fa09 |
0.479 |
0.038 |
12.693 |
0.000 |
0.405 |
0.553 |
| FA |
=~ |
fa10 |
0.592 |
0.033 |
18.218 |
0.000 |
0.528 |
0.656 |
| DE |
=~ |
de01 |
0.561 |
0.033 |
16.808 |
0.000 |
0.495 |
0.626 |
| DE |
=~ |
de02 |
0.700 |
0.026 |
27.265 |
0.000 |
0.650 |
0.751 |
| DE |
=~ |
de03 |
0.609 |
0.031 |
19.717 |
0.000 |
0.548 |
0.670 |
| DE |
=~ |
de05 |
0.519 |
0.035 |
14.677 |
0.000 |
0.450 |
0.588 |
| DE |
=~ |
de06 |
0.639 |
0.029 |
21.837 |
0.000 |
0.582 |
0.696 |
| DE |
=~ |
de07 |
0.602 |
0.031 |
19.273 |
0.000 |
0.541 |
0.663 |
| DE |
=~ |
de08 |
0.690 |
0.026 |
26.215 |
0.000 |
0.638 |
0.741 |
| DE |
=~ |
de09 |
0.310 |
0.043 |
7.191 |
0.000 |
0.225 |
0.394 |
| DE |
=~ |
de10 |
0.716 |
0.025 |
29.002 |
0.000 |
0.668 |
0.765 |
| DE |
=~ |
de11 |
0.243 |
0.045 |
5.436 |
0.000 |
0.155 |
0.331 |
| UN |
=~ |
un01 |
0.740 |
0.022 |
33.462 |
0.000 |
0.696 |
0.783 |
| UN |
=~ |
un02 |
0.831 |
0.016 |
52.491 |
0.000 |
0.800 |
0.862 |
| UN |
=~ |
un03 |
0.544 |
0.033 |
16.332 |
0.000 |
0.478 |
0.609 |
| UN |
=~ |
un04 |
0.735 |
0.022 |
32.798 |
0.000 |
0.691 |
0.779 |
| UN |
=~ |
un05 |
0.806 |
0.018 |
45.760 |
0.000 |
0.771 |
0.840 |
| UN |
=~ |
un06 |
0.494 |
0.036 |
13.895 |
0.000 |
0.425 |
0.564 |
| UN |
=~ |
un07 |
0.687 |
0.025 |
27.059 |
0.000 |
0.638 |
0.737 |
| UN |
=~ |
un08 |
0.752 |
0.021 |
35.289 |
0.000 |
0.710 |
0.794 |
| UN |
=~ |
un09 |
0.690 |
0.025 |
27.358 |
0.000 |
0.641 |
0.740 |
| UN |
=~ |
un10 |
0.710 |
0.024 |
29.592 |
0.000 |
0.663 |
0.757 |
| UN |
=~ |
un11 |
0.772 |
0.020 |
38.620 |
0.000 |
0.732 |
0.811 |
| UN |
=~ |
un12 |
0.737 |
0.022 |
33.023 |
0.000 |
0.693 |
0.780 |
Covariances:
kable(
smodel1 %>%
filter(op == "~~" & lhs != rhs),
col.names = c("Factor", "", "Factor", "Covariance", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)
| PR |
~~ |
CO |
0.674 |
0.032 |
21.123 |
0.000 |
0.611 |
0.736 |
| PR |
~~ |
UT |
0.758 |
0.025 |
29.745 |
0.000 |
0.708 |
0.808 |
| PR |
~~ |
FA |
0.572 |
0.037 |
15.251 |
0.000 |
0.498 |
0.645 |
| PR |
~~ |
DE |
0.886 |
0.019 |
47.788 |
0.000 |
0.849 |
0.922 |
| PR |
~~ |
UN |
0.383 |
0.043 |
8.811 |
0.000 |
0.298 |
0.469 |
| CO |
~~ |
UT |
0.434 |
0.042 |
10.255 |
0.000 |
0.351 |
0.517 |
| CO |
~~ |
FA |
0.493 |
0.041 |
12.022 |
0.000 |
0.413 |
0.574 |
| CO |
~~ |
DE |
0.667 |
0.033 |
20.416 |
0.000 |
0.603 |
0.731 |
| CO |
~~ |
UN |
0.276 |
0.047 |
5.929 |
0.000 |
0.185 |
0.367 |
| UT |
~~ |
FA |
0.451 |
0.042 |
10.813 |
0.000 |
0.369 |
0.532 |
| UT |
~~ |
DE |
0.684 |
0.030 |
22.435 |
0.000 |
0.624 |
0.744 |
| UT |
~~ |
UN |
0.296 |
0.045 |
6.570 |
0.000 |
0.207 |
0.384 |
| FA |
~~ |
DE |
0.659 |
0.033 |
19.845 |
0.000 |
0.594 |
0.724 |
| FA |
~~ |
UN |
0.087 |
0.050 |
1.762 |
0.078 |
-0.010 |
0.185 |
| DE |
~~ |
UN |
0.389 |
0.044 |
8.920 |
0.000 |
0.304 |
0.475 |
Residuals:
kable(
smodel1 %>%
filter(op == "~~" & lhs == rhs)%>%
select(-(2:3)),
col.names = c("Item", "Residual", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)
| pr01 |
0.377 |
0.031 |
12.076 |
0 |
0.316 |
0.438 |
| pr02 |
0.659 |
0.037 |
17.653 |
0 |
0.586 |
0.732 |
| pr03 |
0.912 |
0.026 |
35.640 |
0 |
0.862 |
0.962 |
| pr04 |
0.987 |
0.011 |
92.749 |
0 |
0.966 |
1.008 |
| pr05 |
0.681 |
0.037 |
18.291 |
0 |
0.608 |
0.754 |
| pr06 |
0.655 |
0.037 |
17.542 |
0 |
0.582 |
0.728 |
| pr07 |
0.470 |
0.035 |
13.609 |
0 |
0.402 |
0.537 |
| pr08 |
0.473 |
0.035 |
13.667 |
0 |
0.405 |
0.541 |
| pr09 |
0.661 |
0.037 |
17.694 |
0 |
0.587 |
0.734 |
| pr10 |
0.750 |
0.036 |
20.839 |
0 |
0.680 |
0.821 |
| co01 |
0.454 |
0.036 |
12.730 |
0 |
0.384 |
0.523 |
| co02 |
0.530 |
0.037 |
14.153 |
0 |
0.456 |
0.603 |
| co03 |
0.747 |
0.037 |
20.238 |
0 |
0.675 |
0.820 |
| co04 |
0.887 |
0.029 |
30.728 |
0 |
0.830 |
0.943 |
| co05 |
0.390 |
0.034 |
11.593 |
0 |
0.324 |
0.456 |
| co06 |
0.622 |
0.038 |
16.181 |
0 |
0.546 |
0.697 |
| co08 |
0.887 |
0.029 |
30.806 |
0 |
0.831 |
0.944 |
| co09 |
0.469 |
0.036 |
13.014 |
0 |
0.399 |
0.540 |
| co10 |
0.636 |
0.038 |
16.536 |
0 |
0.560 |
0.711 |
| ut01 |
0.402 |
0.032 |
12.630 |
0 |
0.340 |
0.464 |
| ut02 |
0.301 |
0.027 |
11.006 |
0 |
0.247 |
0.354 |
| ut03 |
0.750 |
0.036 |
20.907 |
0 |
0.679 |
0.820 |
| ut04 |
0.783 |
0.035 |
22.500 |
0 |
0.715 |
0.851 |
| ut05 |
0.579 |
0.037 |
15.774 |
0 |
0.507 |
0.651 |
| ut06 |
0.441 |
0.033 |
13.251 |
0 |
0.376 |
0.506 |
| ut07 |
0.678 |
0.037 |
18.290 |
0 |
0.605 |
0.751 |
| ut08 |
0.611 |
0.037 |
16.492 |
0 |
0.538 |
0.683 |
| ut09 |
0.568 |
0.037 |
15.554 |
0 |
0.497 |
0.640 |
| ut11 |
0.729 |
0.036 |
20.063 |
0 |
0.658 |
0.801 |
| ut12 |
0.524 |
0.036 |
14.683 |
0 |
0.454 |
0.594 |
| fa01 |
0.325 |
0.032 |
10.177 |
0 |
0.262 |
0.387 |
| fa02 |
0.832 |
0.033 |
25.070 |
0 |
0.767 |
0.897 |
| fa03 |
0.969 |
0.016 |
59.581 |
0 |
0.938 |
1.001 |
| fa04 |
0.875 |
0.030 |
29.210 |
0 |
0.816 |
0.934 |
| fa05 |
0.287 |
0.031 |
9.357 |
0 |
0.227 |
0.347 |
| fa06 |
0.640 |
0.039 |
16.624 |
0 |
0.565 |
0.716 |
| fa07 |
0.922 |
0.025 |
37.218 |
0 |
0.874 |
0.971 |
| fa08 |
0.849 |
0.032 |
26.527 |
0 |
0.787 |
0.912 |
| fa09 |
0.770 |
0.036 |
21.268 |
0 |
0.699 |
0.841 |
| fa10 |
0.649 |
0.038 |
16.872 |
0 |
0.574 |
0.725 |
| de01 |
0.686 |
0.037 |
18.318 |
0 |
0.612 |
0.759 |
| de02 |
0.510 |
0.036 |
14.162 |
0 |
0.439 |
0.580 |
| de03 |
0.629 |
0.038 |
16.722 |
0 |
0.555 |
0.703 |
| de05 |
0.731 |
0.037 |
19.910 |
0 |
0.659 |
0.803 |
| de06 |
0.592 |
0.037 |
15.832 |
0 |
0.519 |
0.665 |
| de07 |
0.637 |
0.038 |
16.934 |
0 |
0.564 |
0.711 |
| de08 |
0.524 |
0.036 |
14.436 |
0 |
0.453 |
0.595 |
| de09 |
0.904 |
0.027 |
33.919 |
0 |
0.852 |
0.956 |
| de10 |
0.487 |
0.035 |
13.746 |
0 |
0.417 |
0.556 |
| de11 |
0.941 |
0.022 |
43.330 |
0 |
0.898 |
0.984 |
| un01 |
0.453 |
0.033 |
13.854 |
0 |
0.389 |
0.517 |
| un02 |
0.309 |
0.026 |
11.747 |
0 |
0.258 |
0.361 |
| un03 |
0.705 |
0.036 |
19.471 |
0 |
0.634 |
0.775 |
| un04 |
0.460 |
0.033 |
13.962 |
0 |
0.395 |
0.524 |
| un05 |
0.350 |
0.028 |
12.343 |
0 |
0.295 |
0.406 |
| un06 |
0.756 |
0.035 |
21.495 |
0 |
0.687 |
0.825 |
| un07 |
0.528 |
0.035 |
15.106 |
0 |
0.459 |
0.596 |
| un08 |
0.435 |
0.032 |
13.574 |
0 |
0.372 |
0.498 |
| un09 |
0.524 |
0.035 |
15.035 |
0 |
0.455 |
0.592 |
| un10 |
0.496 |
0.034 |
14.549 |
0 |
0.429 |
0.563 |
| un11 |
0.405 |
0.031 |
13.125 |
0 |
0.344 |
0.465 |
| un12 |
0.458 |
0.033 |
13.925 |
0 |
0.393 |
0.522 |
| PR |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| CO |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| UT |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| FA |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| DE |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| UN |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
Visualization:
semPaths(model1, "std")

Model with general factor
Model:
mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DigTrust =~ PR + CO + UT + FA + DE + UN
"
model2 <- cfa(mdl2, taia %>% select(all_of(taia_items)))
summary(model2)
lavaan 0.6-8 ended normally after 44 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 130
Number of observations 495
Model Test User Model:
Test statistic 6381.193
Degrees of freedom 1823
P-value (Chi-square) 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
PR =~
pr01 1.000
pr02 0.732 0.055 13.227 0.000
pr03 0.389 0.061 6.409 0.000
pr04 0.160 0.063 2.533 0.011
pr05 0.882 0.069 12.845 0.000
pr06 0.797 0.061 13.006 0.000
pr07 1.049 0.061 17.137 0.000
pr08 0.846 0.050 16.877 0.000
pr09 0.719 0.055 13.186 0.000
pr10 0.661 0.060 10.962 0.000
CO =~
co01 1.000
co02 0.894 0.062 14.491 0.000
co03 0.652 0.061 10.699 0.000
co04 0.473 0.065 7.263 0.000
co05 1.088 0.066 16.548 0.000
co06 0.862 0.066 13.089 0.000
co08 0.435 0.062 6.976 0.000
co09 0.977 0.063 15.442 0.000
co10 0.834 0.065 12.798 0.000
UT =~
ut01 1.000
ut02 1.080 0.055 19.757 0.000
ut03 0.674 0.062 10.910 0.000
ut04 0.635 0.062 10.237 0.000
ut05 0.972 0.065 14.837 0.000
ut06 1.007 0.058 17.336 0.000
ut07 0.791 0.062 12.699 0.000
ut08 0.807 0.057 14.089 0.000
ut09 0.946 0.063 14.941 0.000
ut11 0.790 0.068 11.534 0.000
ut12 0.973 0.062 15.774 0.000
FA =~
fa01 1.000
fa02 0.522 0.060 8.760 0.000
fa03 0.204 0.059 3.452 0.001
fa04 0.407 0.055 7.352 0.000
fa05 1.017 0.050 20.447 0.000
fa06 0.704 0.052 13.544 0.000
fa07 0.323 0.056 5.749 0.000
fa08 0.475 0.058 8.239 0.000
fa09 0.616 0.059 10.488 0.000
fa10 0.780 0.058 13.496 0.000
DE =~
de01 1.000
de02 1.314 0.113 11.628 0.000
de03 1.176 0.111 10.576 0.000
de05 1.006 0.104 9.645 0.000
de06 1.267 0.116 10.914 0.000
de07 0.989 0.093 10.609 0.000
de08 1.188 0.103 11.539 0.000
de09 0.623 0.098 6.342 0.000
de10 1.371 0.117 11.764 0.000
de11 0.475 0.096 4.934 0.000
UN =~
un01 1.000
un02 1.212 0.064 18.935 0.000
un03 0.811 0.068 11.917 0.000
un04 1.022 0.062 16.530 0.000
un05 1.140 0.062 18.326 0.000
un06 0.780 0.072 10.831 0.000
un07 1.035 0.067 15.346 0.000
un08 1.118 0.066 16.974 0.000
un09 1.085 0.070 15.412 0.000
un10 1.046 0.066 15.902 0.000
un11 1.188 0.068 17.430 0.000
un12 1.061 0.064 16.562 0.000
DigTrust =~
PR 1.000
CO 0.746 0.061 12.327 0.000
UT 0.818 0.060 13.693 0.000
FA 0.781 0.064 12.133 0.000
DE 0.777 0.066 11.697 0.000
UN 0.408 0.053 7.651 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.pr01 0.369 0.028 13.031 0.000
.pr02 0.616 0.041 14.883 0.000
.pr03 0.935 0.060 15.576 0.000
.pr04 1.070 0.068 15.709 0.000
.pr05 0.966 0.065 14.947 0.000
.pr06 0.763 0.051 14.921 0.000
.pr07 0.573 0.041 13.836 0.000
.pr08 0.393 0.028 13.938 0.000
.pr09 0.599 0.040 14.890 0.000
.pr10 0.810 0.053 15.208 0.000
.co01 0.528 0.040 13.119 0.000
.co02 0.578 0.042 13.832 0.000
.co03 0.775 0.052 14.982 0.000
.co04 1.037 0.067 15.438 0.000
.co05 0.485 0.039 12.362 0.000
.co06 0.759 0.053 14.391 0.000
.co08 0.959 0.062 15.463 0.000
.co09 0.539 0.041 13.290 0.000
.co10 0.761 0.053 14.482 0.000
.ut01 0.442 0.033 13.486 0.000
.ut02 0.336 0.027 12.276 0.000
.ut03 0.925 0.061 15.248 0.000
.ut04 0.959 0.063 15.318 0.000
.ut05 0.836 0.057 14.615 0.000
.ut06 0.528 0.038 13.826 0.000
.ut07 0.864 0.058 15.017 0.000
.ut08 0.673 0.046 14.776 0.000
.ut09 0.775 0.053 14.590 0.000
.ut11 1.106 0.073 15.176 0.000
.ut12 0.690 0.048 14.373 0.000
.fa01 0.375 0.035 10.619 0.000
.fa02 1.162 0.076 15.307 0.000
.fa03 1.250 0.080 15.672 0.000
.fa04 1.036 0.067 15.443 0.000
.fa05 0.348 0.035 10.076 0.000
.fa06 0.748 0.052 14.517 0.000
.fa07 1.097 0.070 15.560 0.000
.fa08 1.100 0.072 15.361 0.000
.fa09 1.074 0.071 15.090 0.000
.fa10 0.927 0.064 14.529 0.000
.de01 0.823 0.055 14.925 0.000
.de02 0.681 0.049 14.028 0.000
.de03 0.904 0.061 14.713 0.000
.de05 0.968 0.064 15.039 0.000
.de06 0.902 0.062 14.545 0.000
.de07 0.630 0.043 14.698 0.000
.de08 0.583 0.041 14.109 0.000
.de09 1.288 0.083 15.533 0.000
.de10 0.687 0.049 13.891 0.000
.de11 1.361 0.087 15.623 0.000
.un01 0.497 0.035 14.367 0.000
.un02 0.399 0.030 13.228 0.000
.un03 0.966 0.063 15.272 0.000
.un04 0.541 0.038 14.422 0.000
.un05 0.423 0.031 13.641 0.000
.un06 1.148 0.075 15.374 0.000
.un07 0.732 0.050 14.740 0.000
.un08 0.581 0.041 14.271 0.000
.un09 0.792 0.054 14.725 0.000
.un10 0.657 0.045 14.604 0.000
.un11 0.584 0.041 14.091 0.000
.un12 0.578 0.040 14.412 0.000
.PR 0.051 0.017 2.954 0.003
.CO 0.324 0.039 8.231 0.000
.UT 0.290 0.033 8.734 0.000
.FA 0.498 0.051 9.744 0.000
.DE 0.043 0.012 3.438 0.001
.UN 0.516 0.055 9.336 0.000
DigTrust 0.552 0.058 9.531 0.000
kable(tibble(
`Model 2` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model2,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))
| Chi-Squared |
6381.1932 |
| DF |
1823.0000 |
| p |
0.0000 |
| GFI |
0.6329 |
| AGFI |
0.6068 |
| CFI |
0.7067 |
| TLI |
0.6957 |
| SRMR |
0.1029 |
| RMSEA |
0.0711 |
semPaths(model2, "std")

Validation
TAIA total score
taia %>%
select(id, all_of(taia_items)) %>%
pivot_longer(all_of(taia_items),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>%
group_by(id, subscale) %>%
summarise(total_score = sum(score)) %>%
pivot_wider(id_cols = id,
names_from = subscale,
values_from = total_score) %>%
full_join(taia) -> taia
clrs <-
c("darkred",
"chocolate3",
"goldenrod3",
"darkgreen",
"darkblue",
"purple4")
Correlations with General Trust Scale
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_l
taia_l %>%
ggplot(aes(score, gt_score, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
scale_color_manual(values = clrs) +
guides(color = FALSE) +
labs(x = "TAIA subscale total score",
y = "General Trust Scale total score",
title = "Corelations between General Trust and TAIA subscales") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$gt_score)
Pearson's product-moment correlation
data: taia$PR and taia$gt_score
t = 3.1872, df = 493, p-value = 0.001528
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.05463758 0.22737163
sample estimates:
cor
0.1420861
cor.test(taia$CO, taia$gt_score)
Pearson's product-moment correlation
data: taia$CO and taia$gt_score
t = 3.842, df = 493, p-value = 0.000138
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.08362567 0.25480562
sample estimates:
cor
0.1705018
cor.test(taia$UT, taia$gt_score)
Pearson's product-moment correlation
data: taia$UT and taia$gt_score
t = 2.4679, df = 493, p-value = 0.01393
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.02255584 0.19668679
sample estimates:
cor
0.110469
cor.test(taia$FA, taia$gt_score)
Pearson's product-moment correlation
data: taia$FA and taia$gt_score
t = 2.0938, df = 493, p-value = 0.03678
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.005800447 0.180524218
sample estimates:
cor
0.0938852
cor.test(taia$DE, taia$gt_score)
Pearson's product-moment correlation
data: taia$DE and taia$gt_score
t = 4.1369, df = 493, p-value = 4.139e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.09659152 0.26698780
sample estimates:
cor
0.183165
cor.test(taia$UN, taia$gt_score)
Pearson's product-moment correlation
data: taia$UN and taia$gt_score
t = 3.5643, df = 493, p-value = 0.0004003
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.07136383 0.24323469
sample estimates:
cor
0.1584997
Correlations with questions
taia_l %>%
ggplot(aes(score, n_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of digital helpers",
title = "Correlation TAIA subscales with number of digital helpers") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$PR and taia$n_dighelp
S = 5561417, p-value = 0.01861
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1281319
cor.test(taia$CO, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$CO and taia$n_dighelp
S = 6618523, p-value = 0.4916
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.03759163
cor.test(taia$UT, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$UT and taia$n_dighelp
S = 5620719, p-value = 0.02917
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.118835
cor.test(taia$FA, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$FA and taia$n_dighelp
S = 6614475, p-value = 0.4989
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.03695705
cor.test(taia$DE, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$DE and taia$n_dighelp
S = 5705718, p-value = 0.05298
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1055096
cor.test(taia$UN, taia$n_dighelp, method = "sp")
Spearman's rank correlation rho
data: taia$UN and taia$n_dighelp
S = 5912050, p-value = 0.1803
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.0731627
taia_l %>%
ggplot(aes(score, e_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with digital helpers experience",
title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$PR and taia$e_dighelp
t = 6.7852, df = 335, p-value = 5.27e-11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2500476 0.4381609
sample estimates:
cor
0.3475971
cor.test(taia$CO, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$CO and taia$e_dighelp
t = 4.0996, df = 335, p-value = 5.199e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1144032 0.3179773
sample estimates:
cor
0.218567
cor.test(taia$UT, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$UT and taia$e_dighelp
t = 5.5088, df = 335, p-value = 7.21e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1871349 0.3832429
sample estimates:
cor
0.288208
cor.test(taia$FA, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$FA and taia$e_dighelp
t = 4.1342, df = 335, p-value = 4.507e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1162249 0.3196358
sample estimates:
cor
0.2203243
cor.test(taia$DE, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$DE and taia$e_dighelp
t = 5.7293, df = 335, p-value = 2.248e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1982204 0.3930214
sample estimates:
cor
0.2987294
cor.test(taia$UN, taia$e_dighelp)
Pearson's product-moment correlation
data: taia$UN and taia$e_dighelp
t = 1.7091, df = 335, p-value = 0.08836
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.01400204 0.19784231
sample estimates:
cor
0.09297223
taia_l %>%
ggplot(aes(score, n_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of social networks and social media",
title = "Correlation TAIA subscales with number of social networks and social media") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$PR and taia$n_socnet
S = 12147347, p-value = 0.01685
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1145436
cor.test(taia$CO, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$CO and taia$n_socnet
S = 14058484, p-value = 0.6065
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.02476493
cor.test(taia$UT, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$UT and taia$n_socnet
S = 11573855, p-value = 0.001069
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1563471
cor.test(taia$FA, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$FA and taia$n_socnet
S = 12379302, p-value = 0.04181
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.09763564
cor.test(taia$DE, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$DE and taia$n_socnet
S = 11425218, p-value = 0.0004627
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1671817
cor.test(taia$UN, taia$n_socnet, method = "sp")
Spearman's rank correlation rho
data: taia$UN and taia$n_socnet
S = 12007801, p-value = 0.009219
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1247154
taia_l %>%
ggplot(aes(score, f_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Frequency of social networks and social media use",
title = "Correlation TAIA subscales with frequency of social networks and social media use") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$f_socnet)
Pearson's product-moment correlation
data: taia$PR and taia$f_socnet
t = 1.4848, df = 433, p-value = 0.1383
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.02299864 0.16409771
sample estimates:
cor
0.07117555
cor.test(taia$CO, taia$f_socnet)
Pearson's product-moment correlation
data: taia$CO and taia$f_socnet
t = 0.83383, df = 433, p-value = 0.4048
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.05418487 0.13355691
sample estimates:
cor
0.0400394
cor.test(taia$UT, taia$f_socnet)
Pearson's product-moment correlation
data: taia$UT and taia$f_socnet
t = -0.18344, df = 433, p-value = 0.8545
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.10275052 0.08527558
sample estimates:
cor
-0.008815392
cor.test(taia$FA, taia$f_socnet)
Pearson's product-moment correlation
data: taia$FA and taia$f_socnet
t = 0.30431, df = 433, p-value = 0.761
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.07950679 0.10849394
sample estimates:
cor
0.01462281
cor.test(taia$DE, taia$f_socnet)
Pearson's product-moment correlation
data: taia$DE and taia$f_socnet
t = 0.51348, df = 433, p-value = 0.6079
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.06951288 0.11841428
sample estimates:
cor
0.02466864
cor.test(taia$UN, taia$f_socnet)
Pearson's product-moment correlation
data: taia$UN and taia$f_socnet
t = -0.29625, df = 433, p-value = 0.7672
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.10811106 0.07989175
sample estimates:
cor
-0.01423547
taia_l %>%
ggplot(aes(score, e_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with recommender systems experience",
title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$e_socnet)
Pearson's product-moment correlation
data: taia$PR and taia$e_socnet
t = 4.6683, df = 433, p-value = 4.056e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1275089 0.3066146
sample estimates:
cor
0.2189049
cor.test(taia$CO, taia$e_socnet)
Pearson's product-moment correlation
data: taia$CO and taia$e_socnet
t = 5.3414, df = 433, p-value = 1.493e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1583093 0.3348224
sample estimates:
cor
0.2486289
cor.test(taia$UT, taia$e_socnet)
Pearson's product-moment correlation
data: taia$UT and taia$e_socnet
t = 4.0939, df = 433, p-value = 5.061e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1008503 0.2819433
sample estimates:
cor
0.1930402
cor.test(taia$FA, taia$e_socnet)
Pearson's product-moment correlation
data: taia$FA and taia$e_socnet
t = 2.5727, df = 433, p-value = 0.01042
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.02901732 0.21425142
sample estimates:
cor
0.1227028
cor.test(taia$DE, taia$e_socnet)
Pearson's product-moment correlation
data: taia$DE and taia$e_socnet
t = 5.7293, df = 433, p-value = 1.89e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1758227 0.3507217
sample estimates:
cor
0.2654548
cor.test(taia$UN, taia$e_socnet)
Pearson's product-moment correlation
data: taia$UN and taia$e_socnet
t = 1.7866, df = 433, p-value = 0.0747
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.008545219 0.178131408
sample estimates:
cor
0.0855438
Self driving cars and education AI
taia %>%
mutate_at(vars(selfdrexp, selfdrsafe, eduaiexp),
function(x) ifelse(x < 0, NA, x)) -> taia
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car experience",
title = "Correlation TAIA subscales with selfdriving car experience") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$PR and taia$selfdrexp
S = 265.3, p-value = 0.3702
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.2711565
cor.test(taia$CO, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$CO and taia$selfdrexp
S = 418.25, p-value = 0.627
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.1490473
cor.test(taia$UT, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$UT and taia$selfdrexp
S = 175.48, p-value = 0.06984
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.5179022
cor.test(taia$FA, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$FA and taia$selfdrexp
S = 272.56, p-value = 0.4077
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.2512199
cor.test(taia$DE, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$DE and taia$selfdrexp
S = 326.24, p-value = 0.7359
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1037321
cor.test(taia$UN, taia$selfdrexp, method = "sp")
Spearman's rank correlation rho
data: taia$UN and taia$selfdrexp
S = 358.61, p-value = 0.9617
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.01481886
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrsafe, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car safe",
title = "Correlation TAIA subscales with selfdriving car safe") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$PR and taia$selfdrsafe
S = 372.22, p-value = 0.9417
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.02257133
cor.test(taia$CO, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$CO and taia$selfdrsafe
S = 323.96, p-value = 0.7205
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1099992
cor.test(taia$UT, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$UT and taia$selfdrsafe
S = 301.67, p-value = 0.576
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.1712239
cor.test(taia$FA, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$FA and taia$selfdrsafe
S = 217.35, p-value = 0.1723
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.4028841
cor.test(taia$DE, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$DE and taia$selfdrsafe
S = 352.98, p-value = 0.9218
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.03026276
cor.test(taia$UN, taia$selfdrsafe, method = "sp")
Spearman's rank correlation rho
data: taia$UN and taia$selfdrsafe
S = 450.47, p-value = 0.4345
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.2375627
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, eduaiexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with education AI experience",
title = "Correlation TAIA subscales with experience of dealing with education AI") +
theme(plot.title = element_text(hjust = .5))

cor.test(taia$PR, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$PR and taia$eduaiexp
S = 136920, p-value = 5.31e-06
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.4152137
cor.test(taia$CO, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$CO and taia$eduaiexp
S = 145289, p-value = 3.687e-05
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3794657
cor.test(taia$UT, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$UT and taia$eduaiexp
S = 142744, p-value = 2.094e-05
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3903391
cor.test(taia$FA, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$FA and taia$eduaiexp
S = 168570, p-value = 0.002786
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.2800356
cor.test(taia$DE, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$DE and taia$eduaiexp
S = 112949, p-value = 5.1e-09
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.5175924
cor.test(taia$UN, taia$eduaiexp, method = "sp")
Spearman's rank correlation rho
data: taia$UN and taia$eduaiexp
S = 159518, p-value = 0.0006156
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3186946